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1. Introduction

The demand for and the consumption of electrical energy have been increasing significantly in recent years [1] due to the growing global population and the development of economic activities [1,2,3]. In fact, Alhasnawi et al. [4] predicts that in the future there will be a shortage of electricity due to the exponential increase in demand by the rapidly increasing world population.

In this scenario, buildings are responsible for a large portion of increased global electricity consumption. According to Amasyali and El-gohary [5], buildings consume 40% of the total energy in the world and contribute to global CO₂ emissions in the same proportion. Thus, improving energy efficiency is recognized as an essential goal to promote the energy sustainability of the planet [6]. For that, actions must be promoted to increase awareness and user participation through more frequent and detailed information about energy consumption [7].

Therefore, electrical energy management in buildings becomes essential to reduce energy demand [4,8] and to improve energy efficiency [9,10]. To do so, it is necessary to determine how the electrical energy is being used, allowing the acquisition of information to propose solutions that increase efficiency [11,12]. From the information on energy use, it is possible to characterize buildings through indicators that can be used to direct the actions of energy management in organizations [13].

An essential part of any smart management system is user involvement, through the provision of associated data and interaction processes [6]. Therefore, energy performance monitoring is important for a smart energy management system [7], as it provides advanced data visualization and analysis tools [13] using smart measurement devices and technologies [7].

Energy management systems have been one of the main issues discussed in the literature in recent years [4,9,14]. In general, the main objectives of these studies are related to power flow monitoring and optimization, for example, by implementing sensing, automation, and demand-side management solutions [6,15]. These studies also present several approaches that can be implemented to improve energy efficiency in buildings, for example, actions related to improving physical facilities or the use of more efficient electrical equipment [16]. In fact, effective energy management practices are necessary to reduce energy consumption and use energy efficiently [3,17,18]. In addition, some studies have focused on complex simulation-based models that rely on information about static perceptions of the environment [6,19].

Other studies use the monitoring of actual electricity consumption to help define the building’s energy profile [10,12]. In this regard, Dell’isola et al. [7] and Vardakas et al. [20] state that the use of real energy consumption data is necessary to achieve proper consumption awareness. Jia et al. [21] and Mariano-Hernández et al. [14] pointed out that there are issues and challenges that still need to be studied in relation to IoT applications, including in relation to energy management. In general, few efforts have been found in the literature to adapt building energy management platforms to provide support for data analytics [22,23], adding value to the process of electric energy management in buildings.

Therefore, seeking to fill this gap in the literature, this study contributes to the investigations on the process of electric energy management in buildings based on the monitoring of real consumption and the Internet of Things. To this end, a systematic literature review was carried out using a rigorous methodological procedure. Specifically, in the selected studies, the different approaches, requirements, and variables involved in the electric energy management process were identified and analyzed.

2. Methodological Approach

A systematic review describes the evidence in a way that allows clear conclusions to be reached about what is already known and what is not known about the subject in question [24]. Thus, a systematic literature review (SLR) is a means of identifying, evaluating, and interpreting all available research relevant to a specific research question, topic area, or phenomenon of interest [25].

In this context, the SLR selects, analyzes, and synthesizes data from the literature on a given theme [26]. It is an approach used in order for the results to be explicit, extensive, and reproducible and to have high-quality studies [27]. This SLR was conducted according to Tranfield, Denyer, and Smart [28] and Denyer and Tranfield [24], whose studies are in line with PRISMA guidelines. Based on that, a four-step research protocol was adopted: planning, search, screening, and analysis, as detailed in Figure 1.

2.1. Planning

This review was guided by a general question of “how does the process of managing electricity in buildings based on the Internet of Things occur?” To be more precise in terms of the scope and focus of the research, the CIMO-logic method was applied, deconstructing the general research question. This model describes the logical thinking of “if one wants to achieve outcome O in context C, use type I intervention” [24]. Thus, it is assumed that a building electricity consumption monitoring initiative (I) can promote energy efficiency (O) based on different indicators (M) and affected by consumption factors (C). Consequently, this allowed deconstructing the above question into five research questions:

Q1:. What are the factors influencing electricity consumption in buildings?
Q2:. What are the elements related to the energy management process?
Q3:. How is the determination of electricity consumption data in buildings carried out?
Q4:. Which indicators are used for monitoring electric energy management in buildings?
Q5:. What is the application of the Internet of Things in energy management?

Considering the research questions, a protocol was designed with an explicit description of the steps performed, as shown in Table 1. To assist in the development of the research protocol, including the definition of guiding questions, keywords, databases, language of publication, time frame, and criteria for selection of articles, some studies related to the theme were considered, such as: Moreno, Zamora, and Skarmeta [29]; Wang, Zhong, and Souri [30]; Jia et al. [21]; Abu Bakar et al. [31]; Terroso-Saenz et al. [22]; Pérez-Lombard, Ortiz, and Pout [32]; Zhao, Zhang, and Liang [33]; Bolchini, Geronazzo, and Quintarelli [34]; Benavente-Peaces [3]; Moreno et al. [6]; Capozzoli et al. [17]; Chung, Hui, and Lam [35]; and Zhu and Li [36].

A total of 11 keywords were selected (indicators, monitoring, “energy efficiency”, “energy management”, “ISO 50001”, “electrical energy”, “energy consumption”, “energy use”, “energy utilization”, “building”, “smart building”). The databases chosen for the research were Scopus and IEEE Xplore, since they are vast, well-evaluated journal indexes, they include high-quality and credible articles, and their scientific content is peer-reviewed [37,38]. As for the criterion of the publication period of the studies, articles published between 2011 and 2022 were considered. Asghari, Rahmani, and Javadi [39]; Wang, Zhong, and Souri [30], and Mendoza-Pitti et al. [40] show that most studies on the Internet of Things applied to smart buildings, smart cities, and energy management in buildings have been published in the last decade.

Once articles that are potential candidates to become primary studies have been obtained, they need to be analyzed so that their relevance is confirmed and papers with little relevance are discarded [28]. Therefore, inclusion and exclusion criteria were defined as a way to select the articles [24,28]. Thus, the selection criteria were defined as follows.

Table 1

Description of the inclusion and exclusion criteria of the articles.

Criterion	Description
Inclusion	(a) Studies dealing with energy consumption, consumption factors, energy indicators, energy monitoring, Internet of Things, and energy management, in the context of electrical energy in buildings.
	(b) Studies that present best practices, case studies, processes, techniques, standards, and tools related to the management of electrical energy in buildings.
	(c) Studies dealing with technologies, protocols, applications, and architectures related to the Internet of Things in the context of monitoring or managing electrical energy in buildings.
Exclusion	(a) Studies that are irrelevant to the research according to the guiding questions, i.e., that do not answer the guiding questions.
	(b) Repeated studies, i.e., the same study is available in different search sources.
	(c) Studies that present incomplete content and inconclusive results, i.e., low-quality works.

2.2. Search

The keywords were combined using the Boolean operator AND, resulting in the construction of 30 search strings to broaden the search for articles [24]. The search strings used based on the keywords selected are detailed in Appendix A. Next, a comprehensive and structured search was performed in the selected databases using the predetermined keywords and search strings [28].

The following criteria were used for the database search:

article-type papers;
published in the last twelve years (from 2011 to 2022);
restricted to the article title, abstract, and keywords;
published in the English language; open access; and
aligned with the research questions and with the inclusion criteria (Table 1).

Thus, using this search strategy, a total of 596 articles were originally identified in the two databases considered. There were 586 (98.32%) articles found in Scopus and 10 (1.68%) articles found in IEEE Xplore.

2.3. Screening

Duplicate articles were removed with the help of Bibliometrix software version 4.1.2 and Excel. Thus, 393 articles were excluded, resulting in 203 articles for analysis through applying the inclusion and exclusion criteria [24,28]. After reading the title and abstract of the 203 articles found, 148 articles were excluded for not addressing issues related to the research theme or for not being considered relevant to the research questions. Therefore, 55 articles were selected for full reading (preselection).

A full review of 55 articles was performed, and 14 articles were discarded for being considered irrelevant to the understanding of the phenomenon studied according to the research questions or for not being available for consultation in the databases. Then, 6 more articles were added as secondary sources through citation analysis (citation tracking), which consists in selecting articles cited among those previously selected from the literature review and that add content to the research [41]. Thus, a final sample of 47 articles was selected for content analysis.

2.4. Content Analysis

The content of the articles was analyzed through bibliometric analysis and thematic analysis, as shown in Table 2. These analyses are approaches commonly adopted in systematic reviews as shown in the studies by Pereira et al. [42]; Mendoza-Pitti et al. [40]; Cozza et al. [43]; and Costa-Carrapiço, Raslan, and Gonzales [44].

The bibliometric analysis aimed to map the production and dissemination of scientific knowledge in the research field [45]. In order to achieve this, we sought to identify the main metrics of sources, authors, and documents (citation analysis), examine the conceptual structure (co-word analysis), and examine the social network structure (co-author analysis), according to Aria and Cuccurullo [46]. To do so, the bibliometric study was conducted using Bibliometrix software. This is an open source software with rich statistical capabilities and it is an excellent choice for scientific computing [46].

Braun and Clarke [47] define thematic analysis as a qualitative method to identify, analyze, interpret, and report patterns, i.e., themes, within data. This method systematizes and describes in a detailed manner a set of data to enable the researcher to interpret different aspects of the research topic [48]. Thus, with this step we aimed to understand the factors related to electrical energy consumption in buildings and its relationship with electricity management indicators and also how the Internet of Things can be used in the process of measuring, monitoring, and managing electrical energy consumption, aligned with the research questions.

Table 2

Content Analysis.

Analysis	Method	Content	Source
Bibliometric	Analysis of key information	Publications over timeNature of the approachScientific methodology usedStudy application	Authors
	Citation analysis	Most productive countriesMost relevant journalsMost cited articles (global)Keywords	Aria and Cuccurullo [46]Zupic and Cater [45]
	Co-citation analysis	Author co-citation network
	Co-word analysis	Co-occurrence network of author keywordsAuthors’ keyword concept map
Thematic	Categorization	Energy consumption factorsStages of the energy management processEnergy consumption dataEnergy consumption indicatorsIoT consumption measurementIoT consumption monitoringDecision-making assessment	Authors

3. Results

This section presents and discusses the results obtained from the bibliometric and thematic analysis in the systematic literature review. The bibliographic analysis made it possible to understand the bibliometric data and describe the most relevant findings of the analyzed sample. On the other hand, the thematic analysis allowed us to understand how the management of electrical energy in buildings based on the concept of the Internet of Things is addressed in the scientific literature.

3.1. Bibliometric Analysis

Figure 2 shows that the scientific production on the topic of the Internet of Things-based electric power management has grown in recent years. While, in 2012, only one paper was published, in 2019, this number rose to ten. From 2020 the number of published papers decreased again. Overall, the scientific production from 2012 to 2022 shows an annual percentage growth rate of 11.61%. This may denote that it is still a topic that needs to be explored and that future research on new points of view is needed or some strands already addressed should be continued. In addition to expanding, it can be stated that research on Internet of Things-based electric power management is an emerging topic, as only 17.00% of the papers were published by 2016, i.e., 83.00% of the articles were published in the last six years. The evolution of research can be justified by the increased interest of researchers due to the need for improvements in the energy management of organizations. In addition, environmental issues associated with the use of technological resources are increasingly part of the discussions at the strategic and academic levels in various institutions.

The scientific method adopted in the sample of selected articles was investigated, as well as the type of building or system analyzed, as shown in Table 3. The types of buildings most analyzed in the articles were: residential buildings (44.68%) and university buildings (23.40%). The classification of buildings without specification represents those where the authors did not detail the type of building or used more than one type. Thus, it was observed that the empirical approach was predominant in 91.49% of the publications. It is noteworthy that most of the published works, 74.47%, adopted the case study [1,49,50,51], which demonstrates the authors’ interest in applying theory to better understand in practice the proposals presented [52]. Only in 2017 were studies that adopted the development of prototypes with applications of experiments published [53], and in 2018 studies that adopted the development of modeling systems with applications of simulations were published [54]. These data suggest that the studies sought to foster improvements in practices applicable to energy efficiency in buildings. Thus, there is a promising field of research on prototypes and systems development.

The results showed a greater participation of countries from the Asian and European continents in the studies analyzed, as shown in Figure 3. In the Asian continent, there were a total of 41 contributions, with the most participation from China (11), Malaysia (7), and Pakistan (7). The European continent presented a total of 38 contributions, with Spain (14), Italy (10), and Poland (4) as the main participants. Next, in the American continent, there are a total of 11 publications, with participation from Canada (7), Barbados (2), Brazil (1), and Colombia (1). Finally, the African continent presented three publications and the participation of Egypt (1), Nigeria (1), and South Africa (1). Thus, there is an opportunity for new research in the American and African continents.

The 47 articles analyzed on this theme were published in 35 different journals. Table 4 lists the respective numbers of publications. In this table, the periodicals Energies and Sensors (Switzerland) are tied at the top of the list of sources with five publications each. Next come Applied Sciences (Switzerland), Energy, Energy and Buildings, and IEEE Access, with two publications each. The rest of the 29 journals appear with one publication each. Thus, it appears that there is no dominant journal in the research area, since the two journals with the biggest number of publications represent, singly, only 10.64% of the published articles.

Table 5 shows the ten most cited articles in the sample in Scopus, showing the influence of the articles. Al-Ali et al. [53] is the most cited article, followed by Gul and Patidar [59] and Shareef et al. [66]. The articles published until 2018 are the most cited and the ones from 2019 onwards have a small number of citations, probably due to the fact that these are more recent. Further, Lin and Sajjad are the most productive authors, both with two published articles in the sample, while the rest of the authors have only one publication each.

The analysis of the most cited references within the sample of 47 selected articles indicated a low bibliographic coupling among the cited studies. The most cited references (fourteen studies) had only two citations each, and the rest had only one each. Analysis of the metadata in the titles, abstracts, and keywords of these most cited studies revealed little relevance to the research. In addition, some studies were published before the period defined in the research protocol, i.e., before 2011.

Figure 4 represents the structure of the co-citation network of authors in the analyzed sample that consists of two clusters where each author represents a node. The diameter of the circles is directly proportional to the number of author citations, and the lines indicate the connections between the authors in that cluster. Author co-citation analysis connects bodies of writing by an author and maps their citation image. Thus, it is possible to identify relevant authors in a knowledge area and connect them through citation records [45]. Therefore, it is possible to infer the formation of two main fields of knowledge, represented by different colors. In the field represented by the blue cluster, the most cited authors are Wang, Zhang, Yang, and Li. In the field represented by the red cluster, the main authors are Chen, Hu, Liu, and Ahmed. The intellectual structure network formed highlighted a scientific collaboration between Wang, Lin, and Capozzoli in the blue cluster and Chen, Al-Ali, and Marinakis in the red one.

Word frequency analysis allows us to classify the research topics, to know which topics are of eminent topicality, and to understand their evolution [69,70]. Table 6 shows the ten most frequent author keywords in the analyzed sample. The author keywords are those provided by the authors of the original articles in the sample. The most used words were energy consumption, energy efficiency, energy management, and the Internet of Things.

A conceptual network was built from the authors’ keywords in order to generate semantic maps with centers of interest of a research area [46,71], as shown in Figure 5. It suggested that there are four ongoing debates. In the purple cluster, the concern is with the Internet of Things related to energy management in smart homes. In the green cluster, there is an interest in studying energy efficiency, especially regarding electricity consumption in smart buildings. In the blue cluster, the focus is on energy management, relating to optimization of energy use and smart grids. Finally, the red cluster addresses the issues of energy, efficiency, and consumption.

Figure 6 presents a conceptual map by grouping commonly co-occurring authors’ keywords, where it is possible to see the formation of two clusters in two dimensions, i.e., the more similar keywords are in distribution, the closer they are represented on the map [46]. In the red cluster are terms related to energy management (energy management system, demand response, energy efficiency), smart buildings (smart grids, smart homes, smart buildings), and the Internet of Things (Internet of Things, IoT, MQTT, artificial intelligence, automation). It is not possible to observe a significant grouping of terms or the formation of a conceptual field in the blue cluster. Thus, there is only one field of study directed to the study of energy management in intelligent buildings based on the Internet of Things (red cluster).

3.2. Thematic Analysis

3.2.1. Electricity Consumption Factors

According to Capozzoli et al. [17], consumption factors directly influence the energy performance of a building, and all of them generate a variety of operational patterns that are not always easy to infer. Batlle et al. [16] complement this by considering that from the analysis of the factors that influence the energy consumption in buildings, it is possible to quantify the savings potential and then propose effective measures to reduce energy consumption.

Table 7 presents the factors that influence energy consumption in buildings. They were classified into four groups: equipment, construction, climate or period, and occupancy. The lighting system was the factor with the highest number of occurrences (14 observations), followed by the heating, ventilation, and air-conditioning (HVAC) system (12), number of occupants in the building (11), use of electrical equipment (10), and weather conditions (9). Regarding occupancy, the “number of occupants” rebounded and was analyzed for all building types.

Some studies highlighted that heating systems [1], ventilation [6], air conditioning [2], lighting [64], and other electrical equipment [56] had a relevant impact on the energy consumption of buildings. Other studies pointed to user behavior [6,18] and climate factors [10,49].

A trend to study heating, ventilation, air-conditioning (AC), and ventilation and air-conditioning (VAC) systems separately was also observed [2,56,72]. However, these systems might be classified into a single category called HVAC systems [1,6]. Moreno et al. [6] considered that HVAC and lighting systems had a direct impact on energy consumption because they were related to the environmental comfort conditions (visual and thermal) of building occupants. Thus, studies on sensing and automation in buildings [1,55] turn to the measurement of variables (light, temperature, humidity, etc.) that are related to these factors. In addition, it was also possible to identify that HVAC systems and lighting are related to the issue of occupant behavior, as they are directly influenced by their comfort preferences [60].

Regarding the studies on electrical equipment, most of these focus on home energy management systems (HEMSs) or demand-side management (DSM) through automatic control of appliances aiming at demand reduction [8,15]. In addition, some studies [49,56] have analyzed the issue of the impact of electrical equipment usage on energy consumption in office buildings.

3.2.2. Elements of the Energy Management Process

Energy management in buildings aims to improve energy efficiency over a long-term period by reducing energy consumption and using energy efficiently, resulting in decreased greenhouse gas emissions and operating costs [17,53]. In the field of energy management, many studies have been conducted to deal with the increasing demand for electricity [15]. However, for Batlle et al. [16], there is no single way to manage the problem of energy consumption in buildings.

In this sense, Figure 7 presents the steps involved in the processes of electric energy management in buildings identified in this SLR. Five necessary steps for an energy management system were established: identification of energy uses [6,16]; assessment of energy consumption [3,7,20]; monitoring energy consumption [49,55]; performance evaluation [4,76]; and implementation of improvements [2,72]. Figure 7 represents the relationship between the steps of the proposed energy management process based on this SLR.

Interestingly, three stages of the energy management process were evidenced more frequently: assessment of energy consumption, with 19 observations; monitoring energy consumption (17); and performance evaluation (19) (Table 8). This result may be related to the fact that most of the analyzed energy management studies address measurement, monitoring, evaluation, and decision processes in an automated way, especially studies that deal with residential energy management systems. On the other hand, the two stages with the lowest frequency of observations are the identification of energy uses and implementation of improvements. Table 8 also shows that most of the studies about energy management were on residential-type buildings (57.89%) and university-type buildings (26.32%). Moreover, only two studies [6,7] applied all six steps of the proposed energy management process. Step 4 appeared in all 19 studies, and in 68.42% of them, the performance evaluation process was carried out in an automated way.

Dell’isola et al. [7] identified the main energy uses (step 1). In that study, the authors consider three main systems related to the energy use of residential buildings: the heating system, the electrical system, and the natural gas system. In another example, Batlle et al. [16] determined that air-conditioning and lighting systems represent the largest contribution in terms of installed load in the university buildings studied. To this end, the authors performed an energy diagnosis by surveying the installed load in each group of existing equipment in these buildings.

Regarding energy consumption assessment (step 2), Capozzoli et al. [17] propose a methodology for characterizing energy consumption profiles over time, based on energy consumption data records, to demonstrate the potential for anomaly detection in building energy management processes. Thus, it is understood that energy management requires reliable numbers to assess and shape energy performance [16]. Therefore, establishing an energy baseline (EBS) from the energy consumption profile is an important step in energy planning, as the EBS acts as a quantitative benchmark that provides the basis of comparison for energy performance [77].

As for monitoring energy consumption (step 3), Jurj et al. [77] proposed an installed building energy management system that allows monitoring through an online visualization of energy data and the generation of local renewable energy sources. According to Moreno et al. [6], real-time information about energy consumption in buildings has been largely invisible to users who have to make do with traditional energy bills. Thus, there is a great opportunity to improve the provision of services that provide greater awareness to building users, especially regarding the energy they consume.

Mahapatra, Moharana, and Leung [67] propose a residential energy management system that provides performance evaluations and intelligent decisions (step 4). The system consists of a service platform that uses an interactive algorithm for users to control energy usage on the demand side. Thus, through monitoring and rate of consumption, the system automatically controls the operation of electrical equipment, reducing consumption at times of high demand.

Finally, the process of implementing improvements (step 6) was observed in four studies dealing with energy management. As an example, Marinakis and Doukas [60] propose an Internet of Things-based system for intelligent energy management in buildings that integrate data from various domains, such as building data, energy production, energy prices, weather data, and user behavior, to produce improvement action plans. In the tool, energy consumption can be evaluated in two different ways: application of inference rules (when the implementation of action plans is simulated) and energy monitoring (when the action plans are actually implemented).

3.2.3. Determining Electric Power Consumption

Table 9 presents data on electricity consumption in buildings relating to consumption determination techniques used, edification type, and study objective. Thirty-nine studies were related to building electric energy consumption in the context of electric energy management, with eleven using strategies geared toward predicting consumption and twenty-eight using strategies geared toward using real-time data.

Solutions for energy consumption prediction have focused on simulation-based models, including statistical models [73], simulation software [72], artificial intelligence-based algorithms [15], and analytical models [51]. According to Moreno et al. [6], simulation-based solutions often rely on very complex predictive models based on the usage profile, climate data, and building characteristics. For example, Zorita et al. [10] developed an energy consumption prediction model considering variables that influence energy consumption in a building (construction characteristics, activities performed, climatic characteristics of the site). In general, studies including consumption determination based on simulations tend to be limited by constraints of the models developed [13] that present complex characteristics due to the various related factors [73]. However, energy demand profiling can be established from the proper treatment of actual consumption data rather than using profiles based on simulations, significantly improving the final modeling of buildings [13].

Solutions that use real-time data on energy consumption in buildings focus on the use of historical consumption data obtained through monthly energy bills or existing databases [77]. Also evidenced in real-time measurement is the use of metering devices [16], sensors [4], and smart meters [8]. For example, Dell’isola et al. [7] investigated consumption through smart meters, processing and transmitting actual energy consumption data to end users through a properly designed feedback strategy.

The study developed by Batlle et al. [16] emphasizes the use of combined techniques of using historical consumption data and metering devices to analyze the actual consumption profile, aiming to define energy baselines through statistical analysis, considering consumption factors. Jurj et al. [77] also used techniques using historical consumption data and metering devices to propose a data cleaning process to improve the quality of baseline data and the evaluation of key performance indicators.

In Table 9, it is possible to verify that the studies that use the statistical modeling technique [19] are related to the application or analysis of models for predicting energy consumption or demand in buildings. In studies using simulation software, there is a trend of research directed towards energy performance analysis [54] and analysis of energy retrofit processes [2]. On the other hand, artificial algorithms based on artificial intelligence are used to predict and manage energy consumption in residential buildings [15]. It is also inferred from Table 9 that, in the studies that use the strategy of using actual consumption data through the analysis of historical data, there is a trend of research pointing to: comparison of consumption through benchmark processes [12,74], analysis of consumption in relation to users’ activities [59,61], and determination of benchmark consumption profiles or patterns [11,13,17,63].

Studies that use sensors or smart meters to obtain actual consumption data analyzed models or energy management systems in real time [61,76]. These studies mostly deal with the problem of demand-side energy management, through consumption monitoring, equipment control, and feedback strategies, especially in residential and office buildings.

Table 9

Strategies for determining electricity consumption data in terms of techniques used, building type, and study objective.

Author	Strategy *	Technical **	Building ***	Goal	Author	Strategy *	Technical **	Building ***	Goal
Muzi, De Lorenzo, and De Gasperis [19]	DP	1	C	Demand forecasting model for electric energy demand	Marinakis and Doukas [60]	DR	7	C	Develop a system that increases the interactivity of energy management systems
Zorita et al. [10]	DP	1	E	Energy consumption forecasting model	Capozzoli et al. [17]	DR	5	F	Propose methodology for characterizing consumption and identifying anomalous energy patterns
Brema and Abraham [64]	DP	2	E	Methodology for energy efficiency strategies	La Puente-Gil et al. [13]	DR	5	E	Method for clustering buildings based on electrical energy consumption
Fichera et al. [51]	DP	4	F	Decision-making method for urban energy strategies	Vardakas et al. [20]	DR	7	F	Propose a co-operative energy management system considering a group of buildings
Xu et al. [54]	DP	2	C	Energy consumption analysis for performance optimization	Esmaeil, Alshitawi, and Almasri [11]	DR	5	B	Analyze consumption patterns, establish indicators, and evaluate the performance of different consumption factors
Hirvonen et al. [72]	DP	2	B	Analysis of energy retrofit processes in relation to energy demand	Kott and Kott [80]	DR	5	B	Analyze energy consumption for energy management system ontology application
Hafeez et al. [15]	DP	3	B	Method for managing the energy use of smart appliances	Garca-Sanz-Calcedo, Gomez-Chaparro, and Sanchez-Barroso [63]	DR	5	E	Analyze the correlation between energy consumption and health activities and propose consumption indicators
Verma, Prakash, and Kumar [57]	DP	3	B	Building management system based on artificial intelligence	Dell’isola et al. [7]	DR	7	B	Investigate the problem of monitoring consumption through a feedback strategy
Nie et al. [73]	DP	1	B	Electricity consumption forecasting model	Chen and Lin [8]	DR	7	B	Analyze demand-side energy management system model
Tabrizchi, Javidi, and Amirzadeh [75]	DP	3	B	Electricity consumption forecasting model	Lin [9]	DR	8	B	Analyze demand-side energy management system model
Ananwattanaporn et al. [2]	DP	2	B	Retrofit analysis of existing buildings	Tientcheu, Chowdhury, and Olwal [55]	DR	7	A	Propose an intelligent system to improve the building’s energy savings
Edwards, Iyare, and Moseley [12]	DR	5	A	Compare energy consumption to determine energy efficiency	Razak and Tan [18]	DR	5	C	Analyze the energy consumption and its relation to the occupancy of the building
Mulville, Jones, and Huebner [49]	DR	6	A	Analyze consumption through feedback and benchmarking processes	Batlle et al. [16]	DR	5–6	C	Analyze the consumption profile to define energy baselines
Moreno et al. [6]	DR	8	F	Analyze energy efficiency through intelligent building management system	Alhasnawi et al. [4]	DR	8	B	Analyze demand-side energy management system model
Gul and Patidar [59]	DR	5	C	Analyze electricity demand profiles and user activities	Hamouda and Dwedar [76]	DR	8	B	Analyze demand-side energy management system model
Chang et al. [68]	DR	7	A	Implement an IoT access point where devices can access the Internet	Abbas [56]	DR	5	A	Investigate the building’s energy conservation potential
Al-Ali et al. [53]	DR	8	B	Propose an energy management system to monitor consumption and control household appliances	Ghajarkhosravi et al. [74]	DR	5	A	Perform energy benchmarking, develop indicators, determine performance rating
Mahapatra, Moharana, and Leung [67]	DR	8	A	Propose an energy management system to control the use of household appliances	Jurj et al. [77]	DR	5–6	C	Propose a data-cleansing process in order to improve the quality of the baseline data
Ouf and Issa [61]	DR	5	D	Evaluate the energy consumption of buildings against other historical benchmarks	Albatayneh [58]	DR	6	B	Determine and analyze energy consumption by building end use
Bastida-Molina et al. [81]	DR	5	C	Electricity consumption analysis methodology based on electricity indicators and standards

* Legend: PV—Forecast data; DR—Real data/** Legend: 1—Statistical model; 2—Simulation software; 3—AI algorithm; 4—Analytical model; 5—Historical consumption data; 6—Metering equipment/devices; 7—Smart meter; 8—Sensors/*** Legend: A—Offices; B—Residential; C—University; D—School; E—Hospital; and F—No specification.

As for the objectives of the studies, one can observe an emphasis on the performance of energy consumption analysis focused on: energy performance evaluation, energy efficiency improvement, baseline determination, development and evaluation of indicators, benchmarking, and feedback processes. In addition, studies on consumption forecasting models and methodologies, energy management systems, determination of consumption patterns, and energy retrofit processes were carried out.

3.2.4. Electric Energy Management Indicators

An effective way to evaluate energy consumption data is through the use of consumption indicators [12]. The development of indicators applicable to energy consumption monitoring should be carried out to make the information understandable to stakeholders [13]. An effective indicator should be easy to use due to the operational data usually being handled by the technical staff and its calculation should be simple to avoid practical difficulties in its implementation [16]. Therefore, appropriate and easy-to-understand indicators should be built to produce the results expected by the stakeholders [7]. According to Zorita et al. [10], the use of indicators allows for presenting the evolution of electricity consumption for each building in a defined time interval and can be used to analyze the building’s energy performance.

Table 10 presents the indicators used for monitoring electric energy management in buildings. The indicators were categorized into three categories, namely: consumption indicator, related to kilowatt-hours (kWh) consumed; environmental indicator, referring to the emission of greenhouse gases (CO₂); and economic indicator, referring to the cost ($) of energy consumed. The most commonly observed consumption indicator is related to energy consumed per unit of area (kWh/m²) with seven occurrences, followed by the indicator related to energy consumption per person (kWh/person) (five observations) and the indicator related to energy consumed per unit of time (kWh/day and kWh/month) (four observations). Other indicators are related to energy consumption in combination with unit of area, time, and people (kWh/m²/year, kWh/m²/month, kWh/m²/person).

Regarding the environmental indicator group, we observe the use of an indicator that measures the emission of greenhouse gases (CO₂) per unit of area (kg.CO₂/m², ton.CO₂/m²), per unit of time (kg.CO₂/year, ton.CO₂/year), and per environment (kg.CO₂/environment). In the economic indicator group, there is an indicator measuring the cost ($) of energy consumption per unit area, per person, and per environment (cost$/m², cost$/person, cost$/environment), where cost$/m² appears most frequently with three observations.

3.2.5. IoT Application across Energy Management

Based on the studies by Alhasnawi et al. [4], Dell’isola et al. [7], and Lin [9], we sought to identify the main characteristics of the architectures of the Internet of Things systems used in the studies selected in this SLR, as shown in Table 11. Sixteen studies were identified that use or suggest some type of measurement to collect real energy consumption data from buildings (data capture), including the measurement process [20,76], sub-measurement [53,68], and both [6,66]. In this scenario, the capture of energy consumption data occurs through sensors [3], energy meters [68], smart meters [8], and smart appliances [15], as described by the authors.

Additionally, regarding the capture of measurement data, it is possible to state that sensors are devices that collect information (voltage, current, power, humidity, temperature, and other variables) from the monitored environment (room, equipment, circuit, and others) and send this information to a specific location with higher data processing capacity, for example, a server computer, smart meter, or web system [4,9,79]. On the other hand, the smart meter differs from the ordinary meter due to its hardware, communication, analysis, and information-processing capabilities [68,78]. It is also understood that smart appliances are electrical equipment, for example, household appliances, equipped with sensors and/or smart meters, which capture and process information related to the use and operation of the equipment [15].

Regarding the communication structure, technology with network standards (Wi-Fi, ZigBee, Bluetooth, RFID, NFC, WSN, Z-Wave, LoRa, NB-IoT), specific communication protocols for the IoT (MQTT, AMQP, CoAP, XMPP, DDSv), and internet protocols (IP, HTTP, Semantic Web) are frequently reported. For Benavente-Peces [3], the main decision for the communication structure is to choose the most appropriate technology. The specifications of each problem impose restrictions that lead to the most suitable technology. The most commonly observed communication patterns in the studies were: Wi-Fi present in eight studies, followed by ZigBee technology (six studies) and Bluetooth technology (three studies).

According to Benavente-Peces [3], these are short-range technologies that operate within a radius of hundreds of meters and can be used for internal communications and around the building, without connection to others and external networks. On the other hand, there are long-range communication standards that are appropriate for interconnecting buildings, i.e., communications between buildings (eg LoRA, GPRS, NB-IoT). These long-range standards are appropriate for applications with smart grids and smart city infrastructures.

Regarding the processing of data collected from sensors and meters (including database creation, data analysis, and application tools), studies have indicated that processing occurs in the cloud [20,53] or at the cloud edge [66,68]. Alhasnawi et al. [4] explains that in cloud analysis, data collected by sensors are transmitted to the cloud (or Internet) via IoT devices, where they are processed through cloud-centric analysis. However, fog (or cloud edge) analytics is an advanced technique used in the analysis of data that must be processed immediately for analytical purposes on IoT devices.

Regarding the monitoring process and user interface, the use of web-based system platforms and the use of local applications are observed. For example, Chen and Lin [8] and Lin [9] used applications based on the ThingSpeak web platform, and Alhasnawi et al. [4] and Mahapatra, Moharana, and Leung [67] used applications based on the Node-Red platform. In another example, Hamouda and Dwedar [76] and Chang et al. [68] developed studies based on local application development (prototype, automation module).

Table 11

Main characteristics of IoT architecture.

Author(s)	Types of Measurement	Characteristics of the IoT Architecture
Author(s)	Types of Measurement	Data Capture	Communication Structure	Data Processing	Monitoring and Interfacing
Alhasnawi et al. [4]	-Sub-metering (equipment)	-Sensors	-MQTT-Wi-Fi	-Cloud -Node-Red platform-Python	-Web system-Node-Red platform
Dell’isola et al. [7]	-Measurement (building)-Sub-metering(equipment)	-Smart meter	-Wi-Fi-ZigBee	-Web system	-Web system
Chen and Lin [8]	-Sub-metering (equipment)	-Smart meter	-HTTP	-Cloud-Edge (gauge)	-Web system (ThingSpeak platform)
Lin [9]	-Sub-metering(equipment)	-Sensors	-HTTP	-Cloud -Edge (devices)	-web system (ThingSpeak platform)
Moreno et al. [6]	-Measurement (rooms)-Sub-metering(equipment)	-Sensors	-IP-HTTP-ZigBee-Bluetooth	-Brim(automation module)	-Automation module developed
Hafeez et al. [15]	-Sub-metering(equipment)	-Smart meter-Smart devices	-Wi-Fi-ZigBee,-HomePlug-Z-Wave	-Cloud	-Simulation-AI-based systems
Benavente-Peces [3]	-Measurement (buildings)-Sub-metering(building systems)	-Sensors	-MQTT (and others)-Wi-Fi-Bluetooth-ZigBee-RFID-NBIoT	-Cloud	-AI-based systems
Hamouda and Dwedar [76]	-Measurement (buildings)	-Smart meter	-MQTT-Wi-Fi	-Edge (developed prototype)	-IoT system (developed prototype)
Vardakas et al. [20]	-Measurement (buildings)	-Smart meter	-IEEE 802.11s	-Cloud	-Cloud-based iot platform
Shareef et al. [66]	-Measurement (buildings)-Sub-metering(equipment)	-Intelligent sensors-Smart meter	-Wi-Fi-Bluetooth-ZigBee	-Cloud -Brim -Java/Python/HTML	-HEMS and DR programs-AI-based controllers
Marinakis and Doukas [60]	-Measurement (buildings)	-Sensors-Historical data	-Semantic web	-Cloud -Java/Python (modules)	-Web system
Mahapatra, Moharana, and Leung [67]	-Measurement (buildings)-Sub-metering(equipment)	-Smart meter-Sensors	-MQTT -Wi-Fi	-Edge (microcont)-Python (algorithm)	-Node-Red (local) platform
Al-Ali et al. [53]	-Sub-metering(equipment)	-Sensors	-MQTT	-Cloud-Javascript	-Web system
Chang et al. [68]	-Sub-metering(equipment)	-Common meter	-Wi-Fi-ZigBee	-Edge (gauge)	-Local comput.
Liaqat et al. [78]	-Measurement (buildings)-Sub-metering (equipment)	-Smart meter-Sensors	-5G	-Cloud-Edge (gauge)	-Web system (utility)
Khan [79]	-Sub-metering(equipment)	-Sensors	-MQTT	-Cloud	-IoT application

3.3. Conceptual Framework

The conceptual framework highlights the main concepts and constructs adopted in research and the relationships between them and can be built in text format and aided by a drawing or schematic framework. Thus, frameworks are used as a way to translate complex themes, enabling them to be studied and analyzed and facilitating interpretations [82].

This section presents a conceptual framework for the management of electric energy in buildings based on the Internet of Things, as shown in Figure 8. To compose the conceptual model, the information obtained from the results of this systematic review was compiled. The proposed framework is composed of two main fields represented by different colors and related to the following themes: energy management (green) and the Internet of Things (yellow).

The field of energy management includes the aspect of energy use, where the energy consumption factors, or main energy uses, are considered. To determine the consumption factors, all elements of the building that contribute to consumption (construction data, location, equipment, operation, users, and activities) are considered. From the characterization of these elements, it is possible to construct a profile of the building, as well as identify opportunities for energy efficiency improvements.

Still in the field of energy management, the aspect of consumption and monitoring includes the steps of obtaining consumption data, through the process of data capture. The next step is to evaluate these data to determine the consumption profile, calculate performance indicators, and create a database, with the help of data-processing/communication tools. Consumption monitoring is carried out through interface tools that allow the user to visualize the information.

In the consumption data aspect, prediction and measurement techniques can be used aiming at simulation strategies or monitoring of actual consumption, or even the use of historical consumption data. This step in the management process is important for defining strategies for database creation and data analysis. In fact, the consumption profile shows the consumption behavior over time or in determined periods, being important for the construction of a baseline or reference line for the definition of management indicators and performance targets.

In turn, the indicators are used to analyze the performance of energy management considering the energy, environmental, and economic aspects. In addition, the indicators are fundamental to the feedback process for the users of the building, supporting the evaluation and decision process. Thus, information about consumption data, consumption profile, and consumption indicators make up a set of elements necessary for the creation of a database that provides a support structure for the evaluation process.

Based on the evaluation process and having as reference the information obtained in the consumption and monitoring stage, it is possible to execute the planning stage and build an improvement plan that aims to implement actions in the search for better efficiency in energy use. The actions proposed in the improvement plan must be aligned with the opportunities identified in the energy use stage and the priorities defined in the evaluation stage.

In the field of energy management, all the steps represented in the conceptual framework represent a continuous improvement process, or plan, do, check, act (PDCA) cycle (Figure 8). Thus, the characterization of these steps is fundamental to efficiently evaluate an existing management process or to plan its implementation to improve the energy efficiency of a building.

The Internet of Things field is represented by three stages that are related to the field of energy management: Data Capture, Communication/Processing, and Interface. In turn, these steps correspond to the levels or layers of Internet of Things architecture. They are the physical level, the logical level, and the interface level. In this configuration, the Data Capture stage, represented by the measurement and sub-metering processes, through sensors or smart meters, is responsible for obtaining energy consumption data.

The captured data are used in the Communication/Processing stage in the process of data analysis and building consumption evaluation. The results of the processing are registered in the database and made available in the Interface stage for visualization on the Internet through systems and web applications. Finally, in the Communication/Processing stage, the network communication standards, communication protocols, programming languages, data storage, analysis, and presentation forms are defined.

Figure 8

Conceptual framework of electrical energy management in buildings based on the Internet of Things.

[Figure omitted. See PDF]

4. Discussion

This theoretical study systematically analyzed how the scientific literature addresses the studies on electrical energy management in buildings based on the Internet of Things. The literature revealed empirical approaches for energy consumption assessment, mainly through case studies directed to the application and use of tools or methodologies developed, considering various building types [17,73]. Examples of identified approaches were modeling of mathematical equations and algorithms and computer simulations, implementation of building automation systems by remote sensing, and analysis of energy consumption data for demand estimation. In general, the proposed solutions seek to direct the implementation of actions to improve energy efficiency and energy management [13].

In general, this systematic review pointed out that the energy consumption factors are more related to the physical characteristics of the building, the activities developed, the equipment used, the climatic factors, and the users’ behavior [4,6]. The respective indicators for monitoring consumption, in general, are related to the value of energy consumed (kWh) per unit of time, built area, population, activity, and end use. These consumption variables are also associated with the building typology: residential, commercial, industrial, educational, hospital, etc. [10].

Based on this review, it was possible to categorize the elements related to the energy management process into five steps: identification of energy uses, assessment of energy consumption, monitoring of energy consumption, performance evaluation, and implementation of improvements. In most studies, only three steps have been used: evaluation of energy consumption, monitoring energy consumption, and performance evaluation. These studies are mainly directed at the automatic energy management process, especially in residential buildings, as exemplified by the studies by Liaqat et al. [78] and Khan [79]. Only the studies by Dell’isola et al. [7] and Moreno et al. [6] considered the five steps of proposed energy management. This result reveals an opportunity for research that considers analyzing aspects of the less commonly observed steps.

Energy consumption data are obtained through predictive analysis or surveys of actual consumption data. Regarding consumption prediction, a common approach identified was the development of models using static variables related to the factors that influence energy consumption. However, most of these studies presented limitations due to the complex and dynamic nature of the variables that need to be considered [10,75]. In addition, studies with a predictive approach consider more specific analyses in the context of buildings, considering limited variables. On the other hand, more robust and realistic results were obtained in the studies that consider actual energy consumption data, where it was possible to obtain a more accurate understanding of the problem studied [7,49]. In addition, the definition of more accurate consumption indicators and more satisfactory performance evaluation tools and feedback were obtained in the studies that consider real energy consumption data [12,63].

Three categories of indicators used in monitoring energy management have been established: consumption, economic, and environmental. The observed consumption indicators (kWh) are measured mainly from building area data (m₂), operating time (day, month, year), and the number of building occupants. The economic indicators follow similar criteria with the measurement of energy costs mainly per unit of area (m₂) and per occupant of the building. The environmental indicators focus on the measurement of carbon dioxide emissions (CO₂) per year and area. In addition, few studies have dealt with economic indicators. Thus, there are opportunities for further research to measure economic and environmental indicators, especially CO₂ emissions per time unit (day, month) and per end use (environment, equipment, occupant).

As for the application of the Internet of Things in energy management, the analyzed studies focused mainly on the development and implementation of solutions directed to the evaluation of efficiency and consumption reduction [75]. Methodologies and tools have been developed that can raise awareness among stakeholders and assist managers in decision making from the energy information made available [16]. To this end, energy performance monitoring was considered important for the implementation of an energy management system, through the use of measurement devices and smart technologies [7]. In this context, the Internet of Things presents itself as a technology in full development and a technically feasible solution to assist in the process of monitoring and managing electricity. Studies show that this technology has been applied in monitoring energy consumption data and building automation systems, in the context of smart buildings and cities [57]. However, further studies on the IoT can be directed toward improving building energy management processes [2,3].

In spite the significant body of literature on electric energy management in buildings, there is an absence of theory explaining the integration of energy management, real-time consumption, and the Internet of Things. Driven by this gap, a conceptual framework was proposed, covering a broad perspective for both researchers and organizations, to understand the aspects involved in the process of electric energy management in buildings based on the monitoring of the real-time consumption based on the Internet of Things. Regarding energy management, the proposed framework describes the elements and their main deliverables, composed of a flow of steps organized in the form of a continuous improvement process. On the other hand, regarding the Internet of Things, the framework presents the phases of the real consumption monitoring process and its main tools. This framework, therefore, represents an integrative view of the energy management process.

5. Conclusions

5.1. Academic and Practical Implications

This study developed a robust theoretical contribution by compiling information and also broadening the range of studies in the area of electric energy management by identifying and condensing information that permeates the study area. Interestingly, until now, based on the scope and breadth of this research, there are no systematic literature reviews that have aimed to categorize the steps involved in the energy management process, especially when using the Internet of Things.

Moreover, this systematic review contributes to the development of theoretical studies on the subject, as identified in the bibliometric analysis. The added scientific value of this research is a better understanding of the process of energy management in buildings and a direction for implementing improvements in this process.

This review also contributes to the improvement of studies that deal with automatic energy management systems, for example, the studies by Lin [9] and Liaqat et al. [78], and demonstrates the importance of considering the identification stage of main energy use as a necessary element to better define the measurement and monitoring needs for more relevant uses. In addition, it emphasizes the importance of the building improvement implementation step to obtain satisfactory results in reducing consumption, as described by Ananwattanaporn et al. [2]. Moreover, it emphasizes the importance of feedback to building users in the performance evaluation stage, through interface tools, as a way to raise awareness about consumption, according to the study by Mulville, Jones, and Huebner [49].

This research contributes to the identification of methodologies and tools for predicting energy consumption data, as presented in the study by Nie et al. [73], as well as ways of determining actual energy consumption, as exemplified in the study by Albatayneh [58]. In addition, it stands out for addressing in an orderly manner the process of energy management in buildings, offering a better understanding of the elements involved and extending analyses performed in the studies by Hafeez et al. [15] and Jurj et al. [77]. Finally, it condenses a larger set of information related to the characterization of factors and indicators of energy consumption according to various building types, extending analyses conducted in the studies of Batlle et al. [16] and Zorita et al. [10].

Similarly, it fills gaps pointed out in the studies of Benavente-Peces [3] and Ananwattanaporn et al. [2] related to the need for conducting research on the application of the Internet of Things in energy management, synthesizing and describing important information that can initiate new research. It also contributes to the development of new energy management applications and tools by describing the main elements and features that make up an Internet of Things architecture, as suggested in the studies by Jia et al. [21] and Terroso-Saenz et al. [22].

The contribution to knowledge and practice of this research is the direction for the implementation of energy management in organizations in the pursuit of energy sustainability, offered by the conceptual framework model developed. This model, besides organizing the information related to the process of energy management based on the Internet of Things, helps in the implementation of this process by identifying and schematizing the analytical variables involved. Thus, the research contributes by offering adequate and robust support for the conduction of energy management actions in the search for improved energy efficiency in organizations.

5.2. Research Limitations and Future Directions

The strengths of this study lie in the methodological process used, the quality of the studies selected, and the extent to which information from these studies integrates. In addition, the number of empirical studies presented in the sample helped to better understand the relationship between the analytical variables considered. However, four limitations were observed in this research which, in turn, drive the need for further research. The first regards the process of searching for studies and choosing the filters to arrive at the sample of articles analyzed. Only studies written in the English language were included, restricted to studies published in the last 12 years, and limited to the use of two databases, which may have resulted in omissions of relevant studies and publication bias.

Second, few articles explicitly explore the elements related to the processes of identifying energy uses and implementing improvements aimed at reducing consumption. In fact, a better understanding of these elements is necessary to improve the conduct of the performance evaluation and planning steps in the management process. Furthermore, it was verified that only one study included analysis of school buildings and there were no studies on industrial buildings. Studies on these types of buildings can contribute to the identification of new factors and indicators of energy consumption. Therefore, the development of new empirical studies is necessary for better analysis and understanding of these elements.

Thirdly, considering the dynamic character of the variables related to the field of the Internet of Things, new variables of analysis may arise as the technological development of measurement, communication, analysis, and data-processing tools occurs. In this sense, one notices a trend for investigations on data analysis at the cloud edge and artificial intelligence applications in the processing, evaluation, and decision phases. Thus, future research can improve this field of research, especially in view of new perspectives regarding the management of smart cities, the development of Industry 4.0, and the use of 5G technology.

Fourth, although the variables selected for the framework can be considered suitable for studies on Internet of Things-based building energy management, it is possible that other variables can also contribute to the framework. For example, it may be necessary to detail the framework and facilitate the understanding process for practical application in organizations. This can be achieved by conducting further empirical and theoretical studies to discover its value and limitations.

In summary, the contribution of this study is the description of the elements of the energy management process in buildings based on real consumption monitoring based on the Internet of Things. It provided a basis for developing a conceptual framework that can be useful due to its practical and theoretical implications. Managers in organizations can use the conceptual framework to implement and evaluate their processes. Furthermore, the scientific evidence can contribute to future research and support public managers in promoting guidelines for improving the energy and environmental performance of buildings. Therefore, this study may contribute to the debate on the sustainable use of electricity in the search for a more sustainable society.

Author Contributions

All elements of the manuscript have been developed with total collaboration between all authors. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

All data and information clarifications can be provided upon reasonable request to corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Systematic literature review protocol. Source: Adapted from Tranfield, Danyer, and Smart [28]; Denyer and Tranfield [24].

Figure 2. Evolution of the number of observations per year.

Figure 3. Scientific production by country.

Figure 4. Authors’ co-citation network.

Figure 5. Co-occurrence network of author keywords.

Figure 6. Conceptual map of the authors’ keywords.

Figure 7. Relationship of the elements of the proposed electricity management process.

Table 3

Number of observations in terms of type of building under study and method of study employed.

Object of Study	N *	Study Method *				Example Authors
Object of Study	N *	1	2	3	4
Office building	5	5	0	0	0	Mulville, Jones, and Huebner [49]King et al. [1]Edwards, Iyare, and Moseley [12]Tientcheu, Chowdhury, and Olwal [55]Abbas [56]
Residential building	21	12	4	3	2	Dell’isola et al. [7]Verma, Prakash, and Kumar [57]Albatayneh [58]Esmaeil, Alshitawi, and Almasri [11]
University building	11	10	1	0	0	Muzi, De Lorenzo, and De Gasperis [19]Gul and Patidar [59]Marinakis and Doukas [60]Capozzoli et al. [17]
School building	1	1	0	0	0	Ouf and Issa [61]
Hospital building	5	5	0	0	0	Cygańska and Kludacz-Alessandri [62]Garca-Sanz-Calcedo, Gomez-Chaparro, and Sanchez-Barroso [63]Brema and Abraham [64]
Building without specification	4	2	0	0	2	Benavente-Peces [3]Fichera et al. [51]Vardakas et al. [20]Parise, Martirano, and Parise [65]
Total	47	35	5	3	4

* Legend: N—Number of observations; 1—Case study; 2—Modeling and simulation; 3—Prototype and experiment; 4—Literature review.

Table 4

Number of publications of the most relevant journals.

Ranking	Periodicals	Number of Publications
1	Energies	5
2	Sensors (Switzerland)	5
3	Applied Sciences (Switzerland)	2
4	Energy	2
5	Energy and Buildings	2
6	IEEE Access	2

Table 5

Top ten most cited articles.

Ranking	Most Cited Articles (Global)	Citations	Citations per Year
1	Al-Ali et al. [53]	251	35.86
2	Gul and Patidar [59]	193	21.44
3	Shareef et al. [66]	153	25.50
4	Marinakis and Doukas [60]	79	13.17
5	Moreno et al. [6]	71	7.10
6	Mahapatra, Moharana, and Leung [67]	61	8.71
7	Capozzoli et al. [17]	43	7.17
8	Chang et al. [68]	39	4.33
9	Ouf and Issa [61]	38	5.43
10	Mulville, Jones, and Huebner [49]	25	2.50

Table 6

Top ten most frequent keywords.

Ranking	Keywords (Author)	Occurrences
1	Energy consumption	8
2	Energy efficiency	6
3	Energy management	6
4	Internet of Things	5
5	Data mining	3
6	Demand response	3
7	Efficiency	3
8	Energy	3
9	Optimization	3
10	Intelligent building	3

Table 7

Factors that influence electricity consumption in buildings.

Group	Consumption Factor	N	Building Type						Examples of Authors
Group	Consumption Factor	N	A	B	C	D	E	F	Examples of Authors
Equipment	Heating, ventilation and air conditioning (HVAC)	6	3	1	1			1	Alhasnawi et al. [4] and Moreno et al. [6] and Razak and Tan [18]
	Heating and ventilation (HV)	2		2					Esmaeil, Alshitawi, and Almasri [11] and Hirvonen et al. [72]
	Air conditioning (AC)	3	1	2					Mulville, Jones, and Huebner [49] and Ananwattanaporn et al. [2]
	Ventilation and air conditioning (VAC)	1	1						Abbas [56]
	Lighting	14	5	4	2		1	2	Dell’isola et al. [7] and Ab Halim et al. [50] and Brema and Abraham [64]
	Electrical equipment	10	4	4			1	1	Alhasnawi et al. [4] and King et al. [1] and Nie et al. [73]
Building	Building area	8		1	2	1	4		Batlle et al. [16] and La Puente-Gil et al. [13] and Ghajarkhosravi et al. [74]
	Building envelope	3	1	2					Ananwattanaporn et al. [2] and King et al. [1] and Hirvonen et al. [72]
	Age of the building	2		1		1			Ghajarkhosravi et al. [74] and Marinakis and Doukas [60] and Ouf and Issa [61]
	Number of floors	1		1					Ghajarkhosravi et al. [74]
	Activities performed	4			1		3		Zorita et al. [10] and Gul and Patidar [59]
Climate or period	Climatic conditions (temperatures)	9	2	2	2		2	1	Zorita et al. [10] and Moreno et al. [6] and Tabrizchi, Javidi, and Amirzadeh [75]
	Seasons (seasonality)	3	1	1	1				Batlle et al. [16] and Tientcheu, Chowdhury, and Olwal [55]
	Time of year (calendar)	1			1				Razak and Tan [18]
Occupation	Number of occupants	11	1	2	3	1	3	1	Tabrizchi, Javidi, and Amirzadeh [75] and Razak and Tan [18] and Ouf and Issa [61]
Occupation	Occupant behavior	5	1	1	2			1	Razak and Tan [18] and Esmaeil, Alshitawi, and Almasri [11]

Legend: A—Offices; B—Residential; C—University; D—School; E—Hospital; and F—No specification.

Table 8

Steps involved in the processes of electrical energy management in buildings.

Author(s)	Building Type	Stages of the Energy Management Process
Author(s)	Building Type	1	2	3	4	5
Alhasnawi et al. [4]	Residential		x	x	x *
Batlle et al. [16]	University	x	x		x
Dell’isola et al. [7]	Residential	x	x	x	x	x
Chen and Lin [8]	Residential		x	x	x
Lin [9]	Residential		x	x	x *
Muzi, De Lorenzo, and De Gasperis [19]	University		x	x	x *
Moreno et al. [6]	No specification	x	x	x	x	x
Hafeez et al. [15]	Residential		x	x	x *
Verma, Prakash, and Kumar [57]	Residential		x	x	x *
Capozzoli et al. [17]	University		x		x
Hamouda and Dwedar [76]	Residential		x	x	x *
Tientcheu, Chowdhury, and Olwal [55]	Office		x	x	x *
Vardakas et al. [20]	No specification		x	x	x *
Jurj et al. [77]	University		x	x	x *
Marinakis and Doukas [60]	University		x	x	x	x
Mahapatra, Moharana, and Leung [67]	Residential		x	x	x *	x
Al-Ali et al. [53]	Residential		x	x	x *
Liaqat et al. [78]	Residential		x	x	x *
Khan [79]	Residential		x	x	x *
Number of observations		3	19	17	19	4

Legend: 1—Identification of energy uses; 2—Evaluation of energy consumption; 3—Monitoring of energy consumption; 4—Performance evaluation; and 5—Implementation of improvements. Note: * automated process (self-analysis and self-decision).

Table 10

Indicators used for monitoring electric energy management in buildings.

Group	Indicator	N	Building Type						Example of Authors
Group	Indicator	N	A	B	C	D	E	F	Example of Authors
Consumption	kWh	1	1						Abbas [56]
	kWh/m²	7	1	3	2	1			Edwards, Iyare, and Moseley [12]and Hirvonen et al. [72]
	kWh/day	6	1	2	3				Moreno et al. [6] and Mulville, Jones, and Huebner [49]
	kWh/month	6		2	2		2		Razak and Tan [18] and Brema and Abraham [64]
	kWh/year	4		2	2				Batlle et al. [16] and Ananwattanaporn et al. [2]
	kWh/m²/year	3	1			1		1	Parise, Martirano, and Parise [65] and Ouf and Issa [61]
	kWh/m²/month	1					1		La Puente-Gil et al. [13]
	kWh/building	2		2					Esmaeil, Alshitawi, and Almasri [11] and Ghajarkhosravi et al. [74]
	kWh/floor	1		1					Ghajarkhosravi et al. [74]
	kWh/environment *	1		1					Dell’isola et al. [7]
	kWh/bed	1					1		García-Sanz-Calcedo, Gómez-Chaparro, and Sanchez-Barroso [63]
	kWh/mesa/day	1	1						Mulville, Jones, and Huebner [49]
	kWh/activity	2		1			1		García-Sanz-Calcedo, Gómez-Chaparro, and Sanchez-Barroso [63]
	kWh/person	6		3	1	1	1		Moreno et al. [6] and Ouf and Issa [61]
	kWh/m²/person **	2			1	1			Marinakis and Doukas [60] and Ouf and Issa [61].
	kWh/use	2		2					Esmaeil, Alshitawi, and Almasri [11] and Albatayneh [58]
	kWh/m²/use ***	2		1	1				Dell’isola et al. [7] and Marinakis and Doukas [60]
	kW/day	1			1				Gul and Patidar [59]
	kW/month	1			1				Gul and Patidar [59]
Environmental	kg.CO₂/m²	2		2					Hirvonen et al. [72] and Ghajarkhosravi et al. [74]
	kg.CO₂/year	1	1						Mulville, Jones, and Huebner [49]
	kg.CO₂/m²/year	1		1					Hirvonen et al. [72]
	kg.CO₂/environment	1		1					Dell’isola et al. [7]
	ton.CO₂eq/m²	1			1				Marinakis and Doukas [60]
	ton.CO₂eq/year	1			1				Batlle et al. [16]
Economic	custo($)/environment	1		1					Dell’isola et al. [7]
	cost($)/m²	3		1	1	1			Marinakis and Doukas [60] and Ouf and Issa [61]
	cost($)/person	1				1			Ouf and Issa [61]
	cost($)/m²/person	1				1			Ouf and Issa [61]

Legend: N—Number of observations; A—Office; B—Residential; C—University; D—School; E—Hospital; and F—No specification. Note: * environment (living room, kitchen, bedroom, bathroom, apartment, other)/** person (per capita, user, occupant, dweller, worker)/*** end use (lighting, air conditioning, heating, equipment, other).

Appendix A

Table A1

Table of strings used in searching for articles.

SEARCH STRINGS—Keyword Combination	Database
Language: English, Period: Last 12 Years, Type: Article—Title, Abstract, Keywords	SCOPUS	IEEE
(indicators) AND (“electrical energy”) AND (“energy consumption”) AND (building)	19	1
(indicators) AND (“electrical energy”) AND (“energy consumption”) AND (“intelligent building”)	0	0
(indicators) AND (“electrical energy”) AND (“energy use”) AND (building)	5	0
(indicators) AND (“electrical energy”) AND (“energy use”) AND (“intelligent building”)	0	0
(indicators) AND (“electrical energy”) AND (“energy utilization”) AND (building)	15	0
(indicators) AND (“electrical energy”) AND (“energy utilization”) AND (“intelligent building”)	0	0
(monitoring) AND (“electrical energy”) AND (“energy consumption”) AND (building)	31	2
(monitoring) AND (“electrical energy”) AND (“energy consumption”) AND (“intelligent building”)	9	0
(monitoring) AND (“electrical energy”) AND (“energy use”) AND (building)	6	0
(monitoring) AND (“electrical energy”) AND (“energy use”) AND (“intelligent building”)	0	0
(monitoring) AND (“electrical energy”) AND (“energy utilization”) AND (building)	25	0
(monitoring) AND (“electrical energy”) AND (“energy utilization”) AND (“intelligent building”)	9	0
(“energy efficiency”) AND (“electrical energy”) AND (“energy consumption”) AND (building)	117	4
(“energy efficiency”) AND (“electrical energy”) AND (“energy consumption”) AND (“intelligent building”)	14	0
(“energy efficiency”) AND (“electrical energy”) AND (“energy use”) AND (building)	35	0
(“energy efficiency”) AND (“electrical energy”) AND (“energy use”) AND (“intelligent building”)	2	0
(“energy efficiency”) AND (“electrical energy”) AND (“energy utilization”) AND (building)	99	0
(“energy efficiency”) AND (“electrical energy”) AND (“energy utilization”) AND (“intelligent building”)	14	0
(“energy management”) AND (“electrical energy”) AND (“energy consumption”) AND (building)	62	3
(“energy management”) AND (“electrical energy”) AND (“energy consumption”) AND (“intelligent building”)	19	0
(“energy management”) AND (“electrical energy”) AND (“energy use”) AND (building)	21	0
(“energy management”) AND (“electrical energy”) AND (“energy use”) AND (“intelligent building”)	3	0
(“energy management”) AND (“electrical energy”) AND (“energy utilization”) AND (building)	60	0
(“energy management”) AND (“electrical energy”) AND (“energy utilization”) AND (“intelligent building”)	18	0
(“ISO 50001”) AND (“electrical energy”) AND (“energy consumption”) AND (building)	1	0
(“ISO 50001”) AND (“electrical energy”) AND (“energy consumption”) AND (“intelligent building”)	0	0
(“ISO 50001”) AND (“electrical energy”) AND (“energy use”) AND (building)	1	0
(“ISO 50001”) AND (“electrical energy”) AND (“energy use”) AND (“intelligent building”)	0	0
(“ISO 50001”) AND (“electrical energy”) AND (“energy utilization”) AND (building)	1	0
(“ISO 50001”) AND (“electrical energy”) AND (“energy utilization”) AND (“intelligent building”)	0	0
TOTAL	586	10

References

1. King, L.S.; Ting, L.W.; Kamarazaly, M.A.; Yaakob, A. Energy-efficient features for office building sustainability in malaysia. Malays. Constr. Res. J.; 2020; 9, pp. 196-207.

2. Ananwattanaporn, S.; Patcharoen, T.; Bunjongjit, S.; Ngaopitakkul, A. Retrofitted Existing Residential Building Design in Energy and Economic Aspect According to Thailand Building Energy Code. Appl. Sci.; 2021; 11, 1398. [DOI: https://dx.doi.org/10.3390/app11041398]

3. Benavente-Peces, C. On the Energy Efficiency in the Next Generation of Smart Buildings—Supporting Technologies and Techniques. Energies; 2019; 12, 4399. [DOI: https://dx.doi.org/10.3390/en12224399]

4. Alhasnawi, B.N.; Jasim, B.H.; Esteban, M.D.; Guerrero, J.M. A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances. Sustainability; 2020; 12, 9686. [DOI: https://dx.doi.org/10.3390/su12229686]

5. Amasyali, K.; El-Gohary, N.M. A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev.; 2018; 81, pp. 1192-1205. [DOI: https://dx.doi.org/10.1016/j.rser.2017.04.095]

6. Moreno, M.V.; Úbeda, B.; Skarmeta, A.F.; Zamora, M.A. How can We Tackle Energy Efficiency in IoT BasedSmart Buildings?. Sensors; 2014; 14, pp. 9582-9614. [DOI: https://dx.doi.org/10.3390/s140609582] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24887040]

7. Dell’isola, M.; Ficco, G.; Canale, L.; Palella, B.I.; Puglisi, G. An IoT Integrated Tool to Enhance User Awareness on Energy Consumption in Residential Buildings. Atmosphere; 2019; 10, 743. [DOI: https://dx.doi.org/10.3390/atmos10120743]

8. Chen, Y.-Y.; Lin, Y.-H. A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management. Sensors; 2019; 19, 4443. [DOI: https://dx.doi.org/10.3390/s19204443]

9. Lin, Y.-H. Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand-side management. Int. Trans. Electr. Energy Syst.; 2019; 29, e12014. [DOI: https://dx.doi.org/10.1002/2050-7038.12014]

10. Zorita, A.L.; Fernández-Temprano, M.A.; García-Escudero, L.-A.; Duque-Perez, O. A statistical modeling approach to detect anomalies in energetic efficiency of buildings. Energy Build.; 2016; 110, pp. 377-386. [DOI: https://dx.doi.org/10.1016/j.enbuild.2015.11.005]

11. Esmaeil, K.K.; Alshitawi, M.S.; Almasri, R.A. Analysis of energy consumption pattern in Saudi Arabia’s residential buildings with specific reference to Qassim region. Energy Effic.; 2019; 12, pp. 2123-2145. [DOI: https://dx.doi.org/10.1007/s12053-019-09806-x]

12. Edwards, E.E.; Iyare, O.; Moseley, L. Energy consumption in typical Caribbean office buildings: A potential short term solution to energy concerns. Renew. Energy; 2012; 39, pp. 154-161. [DOI: https://dx.doi.org/10.1016/j.renene.2011.07.037]

13. De la Puente-Gil, Á.; González-Martínez, A.; Borge-Diez, D.; Blanes-Peiró, J.J.; De Simón-Martín, M. Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study. Environments; 2018; 5, 133. [DOI: https://dx.doi.org/10.3390/environments5120133]

14. Mariano-Hernández, D.; Hernández-Callejo, L.; Zorita-Lamadrid, A.; Duque-Pérez, O.; García, F.S. A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis. J. Build. Eng.; 2021; 33, 101692. [DOI: https://dx.doi.org/10.1016/j.jobe.2020.101692]

15. Hafeez, G.; Wadud, Z.; Khan, I.U.; Khan, I.; Shafiq, Z.; Usman, M.; Khan, M.U.A. Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid. Sensors; 2020; 20, 3155. [DOI: https://dx.doi.org/10.3390/s20113155] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32498402]

16. Batlle, E.A.O.; Palacio, J.C.E.; Lora, E.E.S.; Reyes, A.M.M.; Moreno, M.M.; Morejon, M.B. A methodology to estimate baseline energy use and quantify savings in electrical energy consumption in higher education institution buildings: Case study, Federal University of Itajubá (UNIFEI). J. Clean. Prod.; 2020; 244, 118551. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.118551]

17. Capozzoli, A.; Piscitelli, M.S.; Brandi, S.; Grassi, D.; Chicco, G. Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings. Energy; 2018; 157, pp. 336-352. [DOI: https://dx.doi.org/10.1016/j.energy.2018.05.127]

18. Razak, I.S.A.; Tan, C.S. Impact of electrical energy consumption and occupancy in university building. J. Green Eng.; 2020; 10, pp. 13337-13348.

19. Muzi, F.; De Lorenzo, M.G.; De Gasperis, G. The Impact of Energy Demand Prediction on the Automation of Smart Buildings Management. Int. J. Simul. Syst. Sci. Technol.; 2015; 16, pp. 9.1-9.7. [DOI: https://dx.doi.org/10.5013/IJSSST.a.16.03.09]

20. Vardakas, J.S.; Zenginis, I.; Zorba, N.; Echave, C.; Morato, M.; Verikoukis, C. Electrical Energy Savings through Efficient Cooperation of Urban Buildings: The Smart Community Case of Superblocks’ in Barcelona. IEEE Commun. Mag.; 2018; 56, pp. 102-109. [DOI: https://dx.doi.org/10.1109/MCOM.2017.1700542]

21. Jia, M.; Komeily, A.; Wang, Y.; Srinivasan, R.S. Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications. Autom. Constr.; 2019; 101, pp. 111-126. [DOI: https://dx.doi.org/10.1016/j.autcon.2019.01.023]

22. Terroso-Saenz, F.; González-Vidal, A.; Ramallo-González, A.P.; Skarmeta, A.F. An open IoT platform for the management and analysis of energy data. Futur. Gener. Comput. Syst.; 2019; 92, pp. 1066-1079. [DOI: https://dx.doi.org/10.1016/j.future.2017.08.046]

23. Hashempour, N.; Taherkhani, R.; Mahdikhani, M. Energy performance optimization of existing buildings: A literature review. Sustain. Cities Soc.; 2019; 54, 101967. [DOI: https://dx.doi.org/10.1016/j.scs.2019.101967]

24. Denyer, D.; Tranfield, D. Producing a systematic review. The SAGE Handbook of Organizational Research Methods; Buchanan, D.A.; Bryman, A. Sage: London, UK, 2009; pp. 671-689.

25. Kitchenham, B. Procedures for Performing Systematic Reviews; Keele University: Keele, UK, 2004; Volume 33.

26. Sampaio, R.; Mancini, M. Estudos de revisão sistemÁTICA: Um guia para síntese criteriosa da evidência científica. Rev. Bras. Fisioter; 2007; 11, pp. 83-89. [DOI: https://dx.doi.org/10.1590/S1413-35552007000100013]

27. Okoli, C. A Guide to Conducting a Standalone Systematic Literature Review. Commun. Assoc. Inf. Syst.; 2015; 37, pp. 879-910. [DOI: https://dx.doi.org/10.17705/1CAIS.03743]

28. Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag.; 2003; 14, pp. 207-222. [DOI: https://dx.doi.org/10.1111/1467-8551.00375]

29. Moreno, M.V.; Zamora, M.A.; Skarmeta, A.F. User-centric smart buildings for energy sustainable smart cities. Trans. Emerg. Telecommun. Technol.; 2013; 25, pp. 41-55. [DOI: https://dx.doi.org/10.1002/ett.2771]

30. Wang, D.; Zhong, D.; Souri, A. Energy management solutions in the Internet of Things applications: Technical analysis and new research directions. Cogn. Syst. Res.; 2021; 67, pp. 33-49. [DOI: https://dx.doi.org/10.1016/j.cogsys.2020.12.009]

31. Abu Bakar, N.N.; Hassan, M.Y.; Abdullah, H.; Rahman, H.A.; Abdullah, M.P.; Hussin, F.; Bandi, M. Energy efficiency index as an indicator for measuring building energy performance: A review. Renew. Sustain. Energy Rev.; 2015; 44, pp. 1-11. [DOI: https://dx.doi.org/10.1016/j.rser.2014.12.018]

32. Pérez-Lombard, L.; Ortiz, J.; Pout, C. A review on buildings energy consumption information. Energy Build.; 2008; 40, pp. 394-398. [DOI: https://dx.doi.org/10.1016/j.enbuild.2007.03.007]

33. Zhao, L.; Zhang, J.-L.; Liang, R.-B. Development of an energy monitoring system for large public buildings. Energy Build.; 2013; 66, pp. 41-48. [DOI: https://dx.doi.org/10.1016/j.enbuild.2013.07.007]

34. Bolchini, C.; Geronazzo, A.; Quintarelli, E. Smart buildings: A monitoring and data analysis methodological framework. Build. Environ.; 2017; 121, pp. 93-105. [DOI: https://dx.doi.org/10.1016/j.buildenv.2017.05.014]

35. Chung, W.; Hui, Y.; Lam, Y.M. Benchmarking the energy efficiency of commercial buildings. Appl. Energy; 2006; 83, pp. 1-14. [DOI: https://dx.doi.org/10.1016/j.apenergy.2004.11.003]

36. Zhu, J.; Li, D. Current Situation of Energy Consumption and Energy Saving Analysis of Large Public Building. Procedia Eng.; 2015; 121, pp. 1208-1214. [DOI: https://dx.doi.org/10.1016/j.proeng.2015.09.140]

37. Elsevier. Descubra por que os Principais Pesquisadores e Organizações do Mundo Escolhem a Scopus 2022. Available online: https://www.elsevier.com/solutions/scopus/why-choose-scopus (accessed on 26 July 2022).

38. IEEE. Biblioteca Digital IEEE Xplore. 2022; Available online: https://www.ieee.org/publications/subscriptions/products/mdl/ieeexplore-access.html (accessed on 26 July 2022).

39. Asghari, P.; Rahmani, A.M.; Javadi, H.H.S. Internet of Things applications: A systematic review. Comput. Netw.; 2019; 148, pp. 241-261. [DOI: https://dx.doi.org/10.1016/j.comnet.2018.12.008]

40. Mendoza-Pitti, L.; Calderon-Gomez, H.; Vargas-Lombardo, M.; Gomez-Pulido, J.M.; Castillo-Sequera, J.L. Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review. IEEE Access; 2021; 9, pp. 26119-26137. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3057543]

41. Bakkalbasi, N.; Bauer, K.; Glover, J.; Wang, L. Three options for citation tracking: Google Scholar, Scopus and Web of Science. Biomed. Digit. Libr.; 2006; 3, 7. [DOI: https://dx.doi.org/10.1186/1742-5581-3-7]

42. Pereira, V.; Santos, J.; Leite, F.; Escórcio, P. Using BIM to improve building energy efficiency—A scientometric and systematic review. Energy Build.; 2021; 250, 111292. [DOI: https://dx.doi.org/10.1016/j.enbuild.2021.111292]

43. Cozza, S.; Chambers, J.; Brambilla, A.; Patel, M.K. In search of optimal consumption: A review of causes and solutions to the Energy Performance Gap in residential buildings. Energy Build.; 2021; 249, 111253. [DOI: https://dx.doi.org/10.1016/j.enbuild.2021.111253]

44. Costa-Carrapiço, I.; Raslan, R.; González, J.N. A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency. Energy Build.; 2020; 210, 109690. [DOI: https://dx.doi.org/10.1016/j.enbuild.2019.109690]

45. Zupic, I.; Čater, T. Bibliometric methods in management and organization. Organ. Res. Methods; 2015; 18, pp. 429-472. [DOI: https://dx.doi.org/10.1177/1094428114562629]

46. Aria, M.; Cuccurullo, C. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr.; 2017; 11, pp. 959-975. [DOI: https://dx.doi.org/10.1016/j.joi.2017.08.007]

47. Braun, V.; Clarke, V. Qualitative Research in Psychology Using thematic analysis in psychology Using thematic analysis in psychology. Qual. Res. Psychol.; 2006; 3, pp. 77-101. [DOI: https://dx.doi.org/10.1191/1478088706qp063oa]

48. Nowell, L.S.; Norris, J.M.; White, D.E.; Moules, N.J. Thematic Analysis: Striving to Meet the Trustworthiness Criteria. Int. J. Qual. Methods; 2017; 16, 1609406917733847. [DOI: https://dx.doi.org/10.1177/1609406917733847]

49. Mulville, M.; Jones, K.; Huebner, G. The potential for energy reduction in UK commercial offices through effective management and behaviour change. Arch. Eng. Des. Manag.; 2014; 10, pp. 79-90. [DOI: https://dx.doi.org/10.1080/17452007.2013.837250]

50. Ab Halim, M.F.M.; Yaakub, M.F.; Harun, M.H.; Annuar, K.A.M.; Basar, F.H.; Omar, M.N. An analysis of energy saving through delamping method. Int. J. Electr. Comput. Eng. (IJECE); 2019; 9, pp. 1569-1575. [DOI: https://dx.doi.org/10.11591/ijece.v9i3.pp1569-1575]

51. Fichera, A.; Frasca, M.; Palermo, V.; Volpe, R. An optimization tool for the assessment of urban energy scenarios. Energy; 2018; 156, pp. 418-429. [DOI: https://dx.doi.org/10.1016/j.energy.2018.05.114]

52. Voss, C.; Tsikriktsis, N.; Frohlich, M. Case research in operations management. Int. J. Oper. Prod. Manag.; 2002; 22, pp. 195-219. [DOI: https://dx.doi.org/10.1108/01443570210414329]

53. Al-Ali, A.; Zualkernan, I.A.; Rashid, M.; Gupta, R.; Alikarar, M. A smart home energy management system using IoT and big data analytics approach. IEEE Trans. Consum. Electron.; 2017; 63, pp. 426-434. [DOI: https://dx.doi.org/10.1109/TCE.2017.015014]

54. Xu, X.; Feng, G.; Chi, D.; Liu, M.; Dou, B. Optimization of Performance Parameter Design and Energy Use Prediction for Nearly Zero Energy Buildings. Energies; 2018; 11, 3252. [DOI: https://dx.doi.org/10.3390/en11123252]

55. Tientcheu, S.I.N.; Chowdhury, S.P.; Olwal, T.O. Intelligent Energy Management Strategy for Automated Office Buildings. Energies; 2019; 12, 4326. [DOI: https://dx.doi.org/10.3390/en12224326]

56. Abbas, M.R. Energy conservation potentials of an office buildings in Northern Nigeria: A case study of Katsina secretariat complex. Indones. J. Electr. Eng. Comput. Sci.; 2020; 18, pp. 629-635. [DOI: https://dx.doi.org/10.11591/ijeecs.v18.i2]

57. Verma, A.; Prakash, S.; Kumar, A. AI-based Building Management and Information System with Multi-agent Topology for an Energy-efficient Building: Towards Occupants Comfort. IETE J. Res.; 2020; 69, pp. 1033-1044. [DOI: https://dx.doi.org/10.1080/03772063.2020.1847701]

58. Albatayneh, A. The Share of Energy Consumption by End Use in Electrical Residential Buildings in Jordan. Environ. Clim. Technol.; 2022; 26, pp. 754-766. [DOI: https://dx.doi.org/10.2478/rtuect-2022-0058]

59. Gul, M.S.; Patidar, S. Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build.; 2015; 87, pp. 155-165. [DOI: https://dx.doi.org/10.1016/j.enbuild.2014.11.027]

60. Marinakis, V.; Doukas, H. An Advanced IoT-based System for Intelligent Energy Management in Buildings. Sensors; 2018; 18, 610. [DOI: https://dx.doi.org/10.3390/s18020610]

61. Ouf, M.M.; Issa, M.H. Energy consumption analysis of school buildings in Manitoba, Canada. Int. J. Sustain. Built Environ.; 2017; 6, pp. 359-371. [DOI: https://dx.doi.org/10.1016/j.ijsbe.2017.05.003]

62. Cygańska, M.; Kludacz-Alessandri, M. Determinants of Electrical and Thermal Energy Consumption in Hospitals According to Climate Zones in Poland. Energies; 2021; 14, 7585. [DOI: https://dx.doi.org/10.3390/en14227585]

63. García-Sanz-Calcedo, J.; Gómez-Chaparro, M.; Sanchez-Barroso, G. Electrical and thermal energy in private hospitals: Consumption indicators focused on healthcare activity. Sustain. Cities Soc.; 2019; 47, 101482. [DOI: https://dx.doi.org/10.1016/j.scs.2019.101482]

64. Brema, J.; Abraham, A.A. Energy efficiency analysis of an existing building in a semi-arid region. Ecol. Environ. Conserv.; 2018; 24, pp. S195-S201.

65. Parise, G.; Martirano, L.; Parise, L. A Procedure to Estimate the Energy Requirements for Lighting. IEEE Trans. Ind. Appl.; 2016; 52, pp. 34-41. [DOI: https://dx.doi.org/10.1109/TIA.2015.2463761]

66. Shareef, H.; Ahmed, M.S.; Mohamed, A.; Al Hassan, E. Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers. IEEE Access; 2018; 6, pp. 24498-24509. [DOI: https://dx.doi.org/10.1109/ACCESS.2018.2831917]

67. Mahapatra, C.; Moharana, A.K.; Leung, V.C.M. Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings. Sensors; 2017; 17, 2812. [DOI: https://dx.doi.org/10.3390/s17122812] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29206159]

68. Chang, C.-Y.; Kuo, C.-H.; Chen, J.-C.; Wang, T.-C. Design and Implementation of an IoT Access Point for Smart Home. Appl. Sci.; 2015; 5, pp. 1882-1903. [DOI: https://dx.doi.org/10.3390/app5041882]

69. Liao, H.; Tang, M.; Li, Z.; Lev, B. Bibliometric analysis for highly cited papers in operations research and management science from 2008 to 2017 based on Essential Science Indicators. Omega; 2019; 88, pp. 223-236. [DOI: https://dx.doi.org/10.1016/j.omega.2018.11.005]

70. Caparrós-Martínez, J.L.; Milán-García, J.; Martínez-Vázquez, R.M.; Valenciano, J.d.P. Green Infrastructures and Grand Environmental Challenges: A Review of Research Trends by Keyword. Agronomy; 2021; 11, 782. [DOI: https://dx.doi.org/10.3390/agronomy11040782]

71. Callon, M.; Courtial, J.P.; Laville, F. Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics; 1991; 22, pp. 155-205. [DOI: https://dx.doi.org/10.1007/BF02019280]

72. Hirvonen, J.; Jokisalo, J.; Heljo, J.; Kosonen, R. Effect of apartment building energy renovation on hourly power demand. Int. J. Sustain. Energy; 2019; 38, pp. 918-936. [DOI: https://dx.doi.org/10.1080/14786451.2019.1613992]

73. Nie, P.; Roccotelli, M.; Fanti, M.P.; Ming, Z.; Li, Z. Prediction of home energy consumption based on gradient boosting regression tree. Energy Rep.; 2021; 7, pp. 1246-1255. [DOI: https://dx.doi.org/10.1016/j.egyr.2021.02.006]

74. Ghajarkhosravi, M.; Huang, Y.; Fung, A.S.; Kumar, R.; Straka, V. Energy benchmarking analysis of multi-unit residential buildings (MURBs) in Toronto, Canada. J. Build. Eng.; 2020; 27, 100981. [DOI: https://dx.doi.org/10.1016/j.jobe.2019.100981]

75. Tabrizchi, H.; Javidi, M.M.; Amirzadeh, V. Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation. Evol. Syst.; 2021; 12, pp. 755-767. [DOI: https://dx.doi.org/10.1007/s12530-019-09283-8]

76. Hamouda, Y.E.M.; Dwedar, S.J.I. Optimally Automated Home Management for Smart Grid System Using Sensor Networks: Gaza Strip as a Case Study. Technol. Econ. Smart Grids Sustain. Energy; 2020; 5, 16. [DOI: https://dx.doi.org/10.1007/s40866-020-00089-1]

77. Jurj, D.I.; Czumbil, L.; Bârgăuan, B.; Ceclan, A.; Polycarpou, A.; Micu, D.D. Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings. Sensors; 2021; 21, 2946. [DOI: https://dx.doi.org/10.3390/s21092946]

78. Liaqat, R.; Sajjad, I.A.; Waseem, M.; Alhelou, H.H. Appliance Level Energy Characterization of Residential Electricity Demand: Prospects, Challenges and Recommendations. IEEE Access; 2021; 9, pp. 148676-148697. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3123196]

79. Khan, M.A.; Sajjad, I.A.; Tahir, M.; Haseeb, A. IOT Application for Energy Management in Smart Homes. Eng. Proc.; 2022; 20, 43. [DOI: https://dx.doi.org/10.3390/engproc2022020043]

80. Kott, J.; Kott, M. Generic Ontology of Energy Consumption Households. Energies; 2019; 12, 3712. [DOI: https://dx.doi.org/10.3390/en12193712]

81. Bastida-Molina, P.; Torres-Navarro, J.; Honrubia-Escribano, A.; Gómez-Lázaro, E. Electricity consumption analysis for university buildings. Empirical approach for University of Castilla-La Mancha, campus Albacete (Spain). Renew. Energy Power Qual. J.; 2022; 20, pp. 216-221. [DOI: https://dx.doi.org/10.24084/repqj20.266]

82. Munck, L.; Bansi, A.C.; Dias, B.G.; Cella-de-Oliveira, F.A. Em Busca da Sustentabilidade Organizacional: A Proposição de um Framework. Rev. Alcance; 2013; 20, pp. 460-477. [DOI: https://dx.doi.org/10.14210/alcance.v20n4.p460-477]

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The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption.

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Title

Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review

Author

Gleydson de Oliveira Cavalcanti

; Handson Claudio Dias Pimenta

First page

5753

Publication year

2023

Publication date

2023

Publisher

MDPI AG

e-ISSN

19961073

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.3390/en16155753

ProQuest document ID

2849033352

Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review

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