Systematic Review Analysis on Smart Building: Challenges and Opportunities

Smart building technology incorporates efficient and automated controls and applications that use smart energy products, networked sensors, and data analytics software to monitor environmental data and occupants’ energy consumption habits to improve buildings’ operation and energy performance. Smart technologies and controls are becoming increasingly important not only in research and development (R&D) but also in industrial and commercial domains, leading to a steady growth in their application in the building sector. This study examines the literature on SBEMS published between 2010 and 2020 with a systematic approach. It examines the trend with the annual number of the published studies before exploring the classification of publications in terms of factors such as domain of SBEMS, control approaches, smart technologies, and quality attributes. Recent developments around the smart building energy management systems (SBEMS) have focused on features that provide occupants with an interface to monitor, schedule, and modify building energy consumption profiles and allow a utility to participate in a communication grid through demand response programs and automatic self-report outage functionality. The study also explores future research avenues, especially in terms of improvements in privacy and security, and interoperability. It is also suggested that the smart building technologies’ smartness can be improved with the help of solutions such as real-time data monitoring and machine learning


Introduction
Global warming is widely deemed to be the biggest threat facing mankind [1,2]. Despite the Paris Agreement, the Intergovernmental Panel on Climate Change warns that the world is well short of making due progress towards limiting the rise of temperature to within 1.5 • C. The building sector has to play an essential role in this respect. Globally, it accounts for over one-third of energy consumption, 30% of greenhouse gas emissions, and around 40% of natural resources [3][4][5]. However, a recent investigation [6] shows that the share of building energy consumption has slightly declined over the past decade. The use of energy-intensive appliances such as heating, ventilation, air conditioning (HVAC), refrigeration, cooking, and other miscellaneous loads in the building sector is steadily growing. The situation is intensifying the pressures on the grid, and increasing the broader energy and environmental footprint. In particular, heating and cooling requirements in extreme weather conditions can adversely affect the demand and supply balance and trigger a hike in energy prices. Worldwide, the building industry is pursuing wide-ranging sustainability measures to curtail associated energy and environmental burdens. Sustainable energy solutions, especially in terms of energy efficiency and renewable energy, play a critical role in this respect [5,7].
A smart building energy management system (SBEMS) is a technological advancement that uses technology such as ZigBee specifications to enable different Internet of Things (IoT) products to communicate and share data over a single network being handled by automatic systems [8][9][10]. Sustainable use of energy in buildings is an active area of research across the world. Researchers have explored various approaches to regulate and optimize building energy consumption such as incentive mechanisms, demand response programs, load scheduling, and integration of renewable energy to ensure the grid's sustainability and lower energy consumption costs on the consumer side [11][12][13][14][15][16][17]. Moreover, studies show a large proportion of unnecessary energy consumption can be curtailed via reliable human occupancy detection in the building to avoid energy utilization in unoccupied space [15,18,19].
SBEMS is an active area of research [20,21] and is worthy of in-depth literature review in order to determine important dimensions in terms of the state of affairs and future directions. These studies typically emphasize identifying technological sensors or device implementation weaknesses, ignoring the research limitations that enhance current SBEMS' architecture design, algorithms platform, or models. The SBEMS sector is keen to seek new academic and industry recommendations to improve the performance and functionalities of the SBEMS architecture design and technologies [22,23]. This study provides an in-depth critical review of the literature on SBEMS in terms of research strategies, technologies, and constraints affecting its overall functionality and operation. The study follows the guideline presented by [24,25] for conducting a systematic literature review. It also discusses the research challenges and opportunities in the field of SBEMS. The rest of the paper has seven main sections. Section 2 describes the research methodology; Section 3 describes review planning; Section 4 details the procedure for conducting the literature review; Section 5 details analysis of literature review; Section 6 discusses this study's findings and future research directions, while the last section concludes the paper.

Materials and Methods
The study examined and synthesized the SBEMS literature using a systematic procedure. It follows a three-phase approach proposed by [24], with the breakdown of important stages shown in Figure 1.

Review Planning
The review planning starts with the preliminary investigation process to define the scope and identify the review's primary goal. The initial literature search reveals several gaps that motivate the study to explore the scope and implementation of SBEMS.

Literature Search Process
The search process considers published journal and conference papers, books, reports, and online repositories focusing on smart energy analysis and smart building energy management systems. The search process uses four main keywords ("IoT Energy", "Smarthome", "Smart Home Energy Management System", and "Indoor Comfort Control"). Each of the main keywords consists of sub-keywords selected using guidelines suggested by [24] and experienced derived from [26][27][28]. Each main keyword started broadly to gradually narrow down to more specific terms to cover as many SBEMS technology areas as possible (see Figure 3).
The literature search was performed across five different databases, "Scopus", "Elsevier", ''IEEE Explore", "Science Direct", and "Google Scholar" from the year 2010 to 2020. The search output for "IoT Energy" and sub-keywords resulted in 374 research outputs. Similarly, search results for "Smart Home", "Smart Home Energy Management", and "Indoor Comfort Control", and sub-keywords resulted in 478, 421, and 318 research outputs, respectively. The search process resulted in a total number of 1,594 research outputs. The study moved forward by sorting and removal duplicate literature. This yields the final research outputs to 1,127, eliminating 467 duplicate studies.

Literature Search Process
The search process considers published journal and conference papers, books, reports, and online repositories focusing on smart energy analysis and smart building energy management systems. The search process uses four main keywords ("IoT Energy", "Smarthome", "Smart Home Energy Management System", and "Indoor Comfort Control"). Each of the main keywords consists of sub-keywords selected using guidelines suggested by [24] and experienced derived from [26][27][28]. Each main keyword started broadly to gradually narrow down to more specific terms to cover as many SBEMS technology areas as possible (see Figure 3).

Inclusion and Exclusion Criteria
This review work focuses on the research undertaken around the SBEMS in terms of technologies covering smart indoor comfort control and energy optimization of building appliances. SBEMS studies on network hardware or network connectivity are beyond the scope of this work. Work by [24] has been used as a guideline for the review process in general. The inclusion and exclusion criteria are based on published studies' suggestions and experience [24,26,27,29]. The goal is to ensure that the identified literature meets the review requirements before an in-depth analysis is carried out.
The study examined the total number of 1,127 research outputs after applying inclusion and exclusion criteria as presented in Table 1. The final research output was reduced to 140 studies as shown in Appendix A. The literature search was performed across five different databases, "Scopus", "Elsevier", "IEEE Explore", "Science Direct", and "Google Scholar" from the year 2010 to 2020. The search output for "IoT Energy" and sub-keywords resulted in 374 research outputs. Similarly, search results for "Smart Home", "Smart Home Energy Management", and "Indoor Comfort Control", and sub-keywords resulted in 478, 421, and 318 research outputs, respectively. The search process resulted in a total number of 1594 research outputs. The study moved forward by sorting and removal duplicate literature. This yields the final research outputs to 1127, eliminating 467 duplicate studies.

. Inclusion and Exclusion Criteria
This review work focuses on the research undertaken around the SBEMS in terms of technologies covering smart indoor comfort control and energy optimization of building appliances. SBEMS studies on network hardware or network connectivity are beyond the scope of this work. Work by [24] has been used as a guideline for the review process in general. The inclusion and exclusion criteria are based on published studies' suggestions and experience [24,26,27,29]. The goal is to ensure that the identified literature meets the review requirements before an in-depth analysis is carried out.
The study examined the total number of 1127 research outputs after applying inclusion and exclusion criteria as presented in Table 1. The final research output was reduced to 140 studies as shown in Appendix A. The selected primary research outputs are rated using a quality assessment evaluation (QAE) technique, and the Database of Abstracts of Reviews of Effects (DARE) procedures defined by [24,29]. The QAE in this study uses four (4) quality assessment questions (QA1-QA4), and DARE uses three rating factors (0, 0.5, and 1) to evaluate the level of literature contribution based on the set requirements. QA1: Is the criteria for inclusion and exclusion in the review process well described and appropriate?
The goal is to determine if the inclusion and exclusion criteria is clearly defined and discussed, or is partially implicit and is not defined and hence cannot be readily inferred in the literature.
QA2: Has domain search possibly covered all related work during the literature search?
The goal is to determine whether four (4) or more digital libraries have been searched and some search strategies are added, or all journals addressing the area considered are identified and referenced by authors.
QA3: Have the quality and validity of the included study been assessed by the reviewers? The goal is to determine whether the quality criteria considered are clearly defined and separated from the results.
QA4: Is the information/literature of concern described adequately? The goal is to determine the detailed information presented in the literature. NOTE: Y (yes) indicates inclusion and exclusion criteria are clearly defined in QA1; four or more digital libraries have been searched in QA2; the considered quality criteria considered is clearly defined and separated from results in QA3; and detailed information about the study is presented in QA4. P (partly) indicates partially defined in QA1; three (3) or four (4) digital libraries have been searched in QA2; part of quality criteria is missing or mixed up with results in QA3; and summary information about the study is presented in QA4.
N (no) indicates criteria not defined and cannot be readily inferred in QA1; a maximum of two (2) digital libraries explored with restricted journal access in QA2; quality criteria are completely missing in QA3; and no result or discussion is presented in QA4.
The objective of quality assessment analysis is to identify the level of literature quality (introduction, literature review, material and method, and result discussion) and contribution to the research domain of SBEMS. The assessment results of 188 articles are summarized in Table 2, consisting of 187 related and one non-related research article.

Algorithms
Refers to the logical approach consisting of steps to arrive at a feasible solution to achieving high energy-saving potential S2, S6, S7, S8, S13, S16, S18, S28, S29, S30, S41, S47, S49, S50, S55, S70, S77, S79, S84, S86, S89, S90, S92, S93, S94, S95, S96, S99, S100, S106, S107, S108, S110, S112, S113, S114, S115, S116, S118, S119, S123, S103, S66, S67, S71, S84, S120, S125, S126, S127, S131, S132, S139 53 Survey Refers to the study that provides analytical review on platforms or models, frameworks, and algorithms S1, S17, S35, S36, S37, S38, S46, S51, S57, S60, S62, S68, S75, S80, S111, S131 The assessment results in Appendix A show that 120 pieces of literature scored a 4/4 rating and 38 scored 3.5 out of 4. Likewise, 23 score 3 out of 4 and 6 literature score value 2. 1literature is not relevant, scored 0 out of 4. Works of literature with a score of 4 are literature related to IoT, energy saving in smart buildings, buildings that proposed novel solutions, experimental analysis, evaluation with particular metrics, or critical and analytical presentations of a comprehensive review of the proposed solution. Meanwhile those with a 3.5 score lack intensive experimental analysis or a proper evaluation method. The majority of the pieces of literature with a score of 4 and 3.5 are published journal articles. Likewise, those with a score of 2 present only proof of concept without further experimental analysis and does not fully explore the IoT or energy saving in smart buildings and other buildings. The majority of these pieces of literature are conference papers. In summary, the information in Appendix A reveals that the maximum number of related pieces of literature considered have satisfied quality assessment questions.

Literature Review Analysis
This section analyzes the literature taking into account factors such as trend of publications, their classifications based on the domain of SBEMS, classifications based on the control approach, classifications based on SBEMS technology, and classifications based on quality attributes.

Publication Trends of the Selected Literature
This review paper searched the literature over the last decade. Research on the subject of SBEMS appears to be gaining momentum as indicated by the upward trend shown in Figure 4. It is also found that the concerned publications are dominated by journal articles as compared to the conference papers, as shown in Figure 5. Publications in general are indications of research and development activities in the field in terms of new technologies and applications. The majority of the conference papers are observed to offer conceptual frameworks around individual technology or the application of SBEMS.
Sustainability 2022, 14, x FOR PEER REVIEW literature related to IoT, energy saving in smart buildings, buildings that proposed solutions, experimental analysis, evaluation with particular metrics, or critical and ical presentations of a comprehensive review of the proposed solution. Meanwhil with a 3.5 score lack intensive experimental analysis or a proper evaluation metho majority of the pieces of literature with a score of 4 and 3.5 are published journal a Likewise, those with a score of 2 present only proof of concept without further mental analysis and does not fully explore the IoT or energy saving in smart bu and other buildings. The majority of these pieces of literature are conference pap summary, the information in Appendix A reveals that the maximum number of pieces of literature considered have satisfied quality assessment questions.

Literature Review Analysis
This section analyzes the literature taking into account factors such as trend lications, their classifications based on the domain of SBEMS, classifications based control approach, classifications based on SBEMS technology, and classifications ba quality attributes.

Publication Trends of the Selected Literature
This review paper searched the literature over the last decade. Research on t ject of SBEMS appears to be gaining momentum as indicated by the upward trend in Figure 4. It is also found that the concerned publications are dominated by jou ticles as compared to the conference papers, as shown in Figure 5. Publications in are indications of research and development activities in the field in terms of new nologies and applications. The majority of the conference papers are observed t conceptual frameworks around individual technology or the application of SBEM   The upward trends in literature indicate that SBEMS are becoming increasingly po ular across the world. Technological advancements in the field are enabling users to ma age and control their building energy usage and comfort level automatically, and ev allow it to be controlled remotely through smart phone applications.

Literature Classification Based on Domain of SBEMS
Literature is also classified based on the research domain of SBEMS. A similar clas fication was conducted in [26,27,30] with a goal to investigate which domain of resear topic received more attention in industry and academia. The result of this classification summarized in Table 2.

41
Refers to software infrastructure providing reusable additives or S12, S64, S82, S98, S105, The upward trends in literature indicate that SBEMS are becoming increasingly popular across the world. Technological advancements in the field are enabling users to manage and control their building energy usage and comfort level automatically, and even allow it to be controlled remotely through smart phone applications.

Literature Classification Based on Domain of SBEMS
Literature is also classified based on the research domain of SBEMS. A similar classification was conducted in [26,27,30] with a goal to investigate which domain of research topic received more attention in industry and academia. The result of this classification is summarized in Table 2.
The analysis indicates that 124 articles proposed solutions for smart building applications, while 16 articles presented review analysis to test, validate, and analyze the existing solutions. The analysis further shows that researchers are putting more effort into developing and integrating SBEMS algorithms and platforms or models to optimize building energy consumption. On the other hand, studies based on surveys, architectures, and frameworks received less attention in the research domain of SBEMS. This finding also reveals that most of the SBEMS strategies use an explicit controller that relies on external feedback to manage energy consumption and indoor comfort. This implies that controllers require an appropriate event to trigger energy consumption activities.

Literature Classification Based on Control Approach
Literature is also reviewed according to the strategies for the management of appliances energy consumption. These strategies are classified into predictive (dynamic and static) and non-predictive approaches (see Figure 6). The predictive approach uses automatic systems to control appliances energy consumption based on real-time data collected by sensors known as dynamic approach or appliance energy consumption profile data such as occupancy schedules, fixed setpoint, etc. On the other hand, the non-predictive approach is mostly remote controlled via mobile applications and web services through a central control system such as a smart meter [8,[31][32][33]. In this context, this study classified selected literature into predictive and non-predictive approaches, as presented in Figure 6. The review process reveals that of the 124 publications, 82 focused on commercia buildings implementing predictive approach while the rest mainly dealt with residentia projects through a mix of predictive and non-predictive approaches as shown in Figure 7 Technology that uses a predictive approach can offer enormous energy-saving potentia compared to a non-predictive approach, since the former uses advanced sensing technol ogy, automated software, and hardware controls to maintain the desire comfort level and avoid unnecessary energy usage without occupant intervention. However, non-predictiv approaches offer data analytics capability and proactive response for incentives, mainte nance, indoor comfort adjustment, and utility performance issues. Hence, combining th two approaches in a single control system can represent huge potential for building en ergy-saving, especially in commercial buildings with labs, offices, and business environ ments.  The review process reveals that of the 124 publications, 82 focused on commercial buildings implementing predictive approach while the rest mainly dealt with residential projects through a mix of predictive and non-predictive approaches as shown in Figure 7. Technology that uses a predictive approach can offer enormous energy-saving potential compared to a non-predictive approach, since the former uses advanced sensing technology, automated software, and hardware controls to maintain the desire comfort level and avoid unnecessary energy usage without occupant intervention. However, non-predictive approaches offer data analytics capability and proactive response for incentives, maintenance, indoor comfort adjustment, and utility performance issues. Hence, combining the two approaches in a single control system can represent huge potential for building energy-saving, especially in commercial buildings with labs, offices, and business environments. The review process reveals that of the 124 publications, 82 focused on commercial buildings implementing predictive approach while the rest mainly dealt with residential projects through a mix of predictive and non-predictive approaches as shown in Figure 7. Technology that uses a predictive approach can offer enormous energy-saving potential compared to a non-predictive approach, since the former uses advanced sensing technology, automated software, and hardware controls to maintain the desire comfort level and avoid unnecessary energy usage without occupant intervention. However, non-predictive approaches offer data analytics capability and proactive response for incentives, maintenance, indoor comfort adjustment, and utility performance issues. Hence, combining the two approaches in a single control system can represent huge potential for building energy-saving, especially in commercial buildings with labs, offices, and business environments.

Classification Based on SBEMS Technology
The literature reviews also looked into the smart building technologies as shown in Table 3, which distributes the literature according to different types of technologies.
The smart HVAC system's primary goal is to offer high energy efficiency and acceptable indoor comfort satisfaction. A recent review in [34] shows that HVAC system offered the functionality to remotely control and manage room temperature and air quality through collaboration with carbon dioxide and thermal sensors installed in the room. In this context, this study classified this technology into three categories based on data used as input to control HVAC energy consumption. The first category [15,16,18,[35][36][37][38][39][40][41][42][43][44] of the control system uses temperature and humidity data. The second category [45][46][47][48] uses temperature, humidity, and an infrared camera. The third category uses temperature, humidity and a carbon dioxide sensor data [32,[49][50][51][52][53][54][55][56][57][58]. It is observed that over the years, smart HVAC systems' performance has gradually improved through advanced control strategies whereby ambient conditions and occupants' energy profiles become an integral part of the system. However, most of the first category solutions cannot fully reflect precise thermal comfort sensations and result in higher discomfort and more energy usage. In the first category's advancement, the second and third categories are introduced to count indoor occupant numbers to adjust ventilation levels under satisfactory comfort level and to avoid vacant space ventilation.
The realization of smart lighting solutions is growing. Presently, several smart lighting products are available with various features. For example, Logitech POP smart button and Philips Hue-Go brighten the house and add ambiance and elegant colors to create a calm and energy-aware environment to support indoor relaxation and social activities [59]. The functionality of smart lighting depends on device-type features. The level of smartness in lighting equipment is also advancing.
Smart plugs are the primary building energy efficiency contributor by stripping smart appliances power when they are no longer required without being in arms or even at home. Some of the existing approaches use user behavior towards energy use [50,[60][61][62][63][64], and smartphones [65][66][67] to turn the appliance on or off. Another approach uses external data such as energy price to control the energy consumption of appliance [68].
Smart windows can monitor important factors regarding the buildings' status (i.e., occupants' window opening behavior, occupancy, indoor temperature and air quality, illumination level, and energy consumption profile) and take decisions to maintain acceptable indoor comfort to save energy. However, a limited number of studies integrate window opening behavior with controlling indoor ventilation and energy saving. Several studies, including [69], have indicated that occupants' ventilation habits have a significant impact on energy saving, especially in terms of window-opening activities.
A smart energy optimization solution is an effective way to regulate the mismatch between energy generation and demand via an incentive mechanism for the customer to lower their energy consumption load during peak demand (extreme hot or cold hours period) to ensure smart grid sustainability [12,70]. The demand-side response is mostly applied in smart grid applications to balance demand and supply by encouraging customers to modify their load profile, for example, by scheduling the load to a period of low energy demand [32] to benefit from low tariff structures or adjusting the desired comfort level by minimizing the setpoint energy consumption. For example, the existing alternative to solve power imbalance and high cost by shifting the load compromises the comfort by deviating from desirable setpoint [42,57,58,[71][72][73][74][75][76][77][78]. In the literature [57,79,80], fuzzy logic has been used to control HVAC systems' energy consumption using a programmable microcontroller to adjust temperature set point changes. The programmable thermostat provides the occupant with the capability to lower the energy consumption or schedule the demand during a mismatch period.
Efficient human operation is essential to analyze reports and alerts received from building appliances through a smart interface allowing building operation to modify installed appliances' energy consumption profile. Several studies [39,70,[81][82][83][84][85][86] used programmable controllers and mobile applications to create schedules based on appliances energy consumption or priority. Studies [78,[87][88][89][90][91][92] proposed a controller that provides functionality for building operators to adjust setpoints to minimize energy consumption based on incentives or alerts received from grid. Other studies used different operational strategies [44,[93][94][95][96][97][98][99][100] to monitor indoor climate and other energy consumption-related data from the appliance through a web service dashboard allowing the building operator to act on data to reduce energy consumption.
Research on integrating renewable energy sources such as solar PV, wind turbines, and air and ground source heat pumps is quite established. Renewable technologies can help ensure grid sustainability especially during the peak demand periods. In many cases, to ensure the grid's sustainability, customers are presented with an incentive or alert to lower their energy consumption to avoid a higher cost of electricity rate [10,13,70,[101][102][103]. Studies in [49,[104][105][106][107][108][109][110][111] leverage the concept of the IoT to monitor and collect data on utilities, smart grid, and other distributed energy sources to quickly detect and resolve fault and service issues through continuous self-assessment. This innovative concept makes it possible for utilities to automatically self-report outage without occupant intervention. Its integration in a smart grid is essential to improve communication among utilities and smart and self-healing functionality.

Classification Based on Quality Attribute
A quality attribute is a testable property of the proposed system that can indicate how the system satisfies the primary goal. This review classified selected literature based on four (4) quality attributes (security, privacy, interoperability, and scalability). The result of this classification is summarized in Table 4.
The findings show that researchers are paying attention to performance efficiency, interoperability, and scalability with less attention to security and privacy. Even though there is little literature addressing the scalability and interoperability among smart building technologies, SBEMS technologies and products currently lack a standard protocol to understand and communicate with a single system [7,112]. Therefore, there is still demand to extend interoperability and scalable features for SBEMS to incorporate multiple appliances from different vendors and technologies and provide mutual communication protocol compatible with all appliances. Due to the lack of standard security design and constraints in SBEMS architecture requirements, the security and privacy concerns are significantly increasing, affecting the reputation of SBEMS [113][114][115]. Table 4. Literature evaluation based on software quality attributes.

Discussion and Future Recommendations
A smart building energy management system (SBEMS) is the application of the internet of things (IoT) that helps optimize the energy consumption in smart building through the implementation of robustly designed control strategies. There are different types of SBEMS technologies to automatically and continuously optimize building energy consumption and to maintain indoor comfort to a satisfactory level. This study provides a review of the recent literature on SBEMS. It identifies the publication trends in terms of number of the annually published studies, considering both journal articles and conference papers. With the help of the set review strategy, the study also classifies the published literature in terms of factors such as domain of SBEMS, control approaches, smart technologies, and quality attributes.

Reporting Finding
The study shows that most of the literature in the domain of SBEMS is focused on solutions that use an explicit controller that requires input feedback from the user or building operator when an incentive from the grid to schedule demand or lower the building energy consumption. There is a need to introduce advanced machine learning in smart building technologies to respond to incentives as users might be too busy to respond or may miss incentives.
The study shows that the strategies used to manage energy consumption in commercial buildings are typically different from those in the residential buildings, emphasizing the need for smart energy management solutions in both types of buildings. We suggest an improved SBEMS that implements commercial building and residential strategies since commercial building energy management solutions might be unreliable or cannot guarantee satisfactory results in residential buildings. Most commercial building solutions rely on static data such as schedule activities and timers to respond to energy usage [31,57].
Satisfactory thermal comfort is one of the primary goals of HVAC systems. However, many of the HVAC systems deployed in commercial buildings implement a fixed set point, which can lead to undesirable indoor thermal conditions when there is unexpected variation in occupancy or weather conditions. It is, therefore, suggested that to optimize energy consumption and to improve thermal comfort, HVAC strategies should include occupancy responsive control that can adjust setpoint temperature of the system based on influencing internal (internal gains and occupancy) and external factors (i.e., weather conditions).
Demand control ventilation strategy monitors and regulates HVAC ventilation setpoints automatically and continuously in proportion to the occupant's carbon dioxide (CO 2 ) generation rate. This practice reduces excess energy consumption due to over ventilation, primarily if the building has been occupied by less than half of the designated number of occupants.
A recent study in [116] proposed an energy price responsive controller to manage the operating frequency of HVAC systems based on electricity price from the grid using 5-min time intervals. The self-response feature can prevent an occupant from operating the HVAC system at a higher frequency when electricity costs are high. It is also essential to integrate a standard pricing scheme that allows occupants with an estimated energy consumption price rate per for a day ahead to enable an occupant to plan for using energy-intensive appliances. For example, historical energy consumption data and other dependent variables can be used to generate a model that predicts the energy consumption rate in order to pump water on storage tankers during a particular period. Enabling this feature would allow an occupant to accurately schedule energy-intensive appliances to benefit from the low-tariff periods.
Future smart building technologies require the integration of enhanced occupancy detection and prediction technology. The current technologies commonly used in SBEMS to detect and predict occupancy employ passive infrared sensors and CO 2 monitoring, and motion detection sensors to optimally manage energy consumption. These devices presently cannot differentiate human and non-human occupancy. Therefore, occupancy detection algorithms to distinguish between human and non-human occupancy are essential to avoid HVAC load in unoccupied spaces.
Similarly, most of the existing research on thermal comfort assessment uses a thirdparty device such as a wearable device to asses skin temperature to maintain occupant's desired temperature [117]. Many third-party devices can communicate and share data with manufacturers or cloud services. Therefore, researchers need to pay more attention to contactless thermal comfort research to assure occupants' privacy, which will result in wider acceptance of SBEMS [118,119].
Research on smart indoor lighting technology is gaining momentum, and needs to play an important role in the development of solutions for smart buildings. Currently, a smart building utilizes modern lumen, dimmers, and LEDs [120]. However, today's lighting system accounts for high energy consumption in buildings due to poor lighting control practices and issues with building design. Synchronizing lights to their companion applications and remote control can be used to improve lighting control. Daylight and motion detectors can help significantly reduce the lighting energy loads [121]. Good window insulation and right skylight positioning, combined with dimmers, can help cut lighting energy consumption by around 40% [122].
The current smart plug is essential to switching off an appliance. The smartness of smart plug technology can be improved to provide scheduling functionality to utilize shiftable appliances. Interoperability features can also enhance collaboration among different SBEM products and receive instruction from virtual assistants, voice control AI, and smart thermostats.
Embedding deep learning in the smart plug can help smart plugs learn and adapt to occupants' schedules in terms of demand on the appliance. For example, if the occupants set a gym workout on the calendar, the washing machine connected with a smart plug should turn on the wash as the occupant returns home. This features an essential way to save energy, especially on frequently used appliances such as a coffee maker or the living room light.
Integrating occupant behavior into smart window systems can enhance building energy efficiency. This can be achieved through occupant behavior relating to windowopening strategies [6][7][8]. Therefore, it is essential for researchers to focus on understanding and modelling occupant window-opening behavior through machine learning techniques to obtain daily information.
Other smart energy optimization solutions [117,123] require wearable or third-party devices to track an occupant's current location to operate ahead of the occupant's arrival, which significantly reduces energy consumption. These approaches pose a threat to occu-pants' privacy. Therefore, there is a need for self-leaning and an adaptive smart energy optimization system with more reliable and accurate occupant whereabouts prediction. This will help start HVAC equipment at low temperatures for heating or cooling activities ahead of arrival at home to stabilize the room temperature to the desired level, saving more energy than stating HVAC equipment at an aggressive temperature setpoint.
The smart grid power imbalance can be curtailed with improved demand response solutions that allow occupants to generate their energy locally through renewable sources such as rooftop solar panels, reducing their reliance on the grid. Schemes such as feed-intariff and net-metering renewable sources can also lead to revenue generation.
Security has been a real issue since the inception of smart buildings, an issue which is more important especially in terms of data sharing and communication. Smart CCTV cameras, voice control assistants, and thermostats are becoming prime targets for cyberattacks resulting in issues such as denial of service and intrusion of privacy due to lack of security mechanism and infrastructure [113]. The dimensions of new vulnerabilities and attack-vectors are likely to increase in future due to the ongoing deployment of SBEMSs without duly accounting for security requirements [124,125]. A few studies [61,126] with security schemes lack universality and are usually applied to secure special communication or applications domain. Currently, studies in the field of SBEMS lack attention in terms of security requirements. Future research needs to pay more attention to this subject in order to mitigate cyber-attacks and to provide mechanisms for the immediate detection and reporting of a security breach.
Another challenge is privacy. For example, a smart meter can be connected with a smart appliance that is responsible for tracking resident behavior patterns, vital body signs such as blood pressure, heart rate, body temperature, drug overdose, muscle strength, sleeping hours, medical prescription-such a device is prone to a privacy breach. It is essential to know this information dynamically and share it with energy providers and other relevant service providers or in the cloud for data analytics. Current practice exposes vital occupant personal data directly without ensuring privacy.
Scalability is a significant factor that ensures the universal expansion of the SBEMS. Today, the high demand for smart energy management system infrastructure can accumulate newly added smart appliances for better energy management operation. Current lighting technology in the market has a limited number of lighting bulbs and accessories to accommodate for [127,128], such as replacement traditional lighting within the building that is not often used. For instance, lighting in the bathroom, kitchen, hallway, garage, and store can be replaced with motion sensor couples lights, while places such as the living room and bedroom can be replaced with dimmer lights for efficient energy usage. Increasing the current lighting system's scalability would increase the capacity of the number of lighting appliances that can be connected and controlled by a single, smart lighting system to manage motion detection and dimmers lighting devices.
Current SBEMS provide limited interoperability functionalities. This issue is among the major obstacles that hinder the development of complex SBEMSs with broader functionalities. The issue can be expressed as a language barrier among smart building appliances. For example, communication between the lighting system and the HVAC system requires language understanding. Future smart buildings demand technologies that communicate, recognize, and connect different building appliances regarding their functions and vendors.
Integrating some initiatives such as IEEE P2413, ZigBee Alliance, X10, and Z-Wave [129] can enable standard unified architecture models to support interoperability among a diversity of smart appliances regardless of their specification or manufacturers, promoting universal growth for future SBEMSs.

Implications of the Study
The present study offers valuable insight into the existing body of literature on the subject of SMS. It can benefit not only the concerned academic and research community, and developers/manufacturers of smart building technologies, but also the broader building industry stakeholders on multiple fronts. Firstly, this study provides a comprehensive review of literature covering various smart building technologies in terms of their functions and application. Secondly, it presents an analytical procedure to identify the gaps in the literature. It is observed, for example, that existing literature is typically focused on individual technologies or applications, and there is a lack of studies covering multiple dimensions of SBEMS simultaneously. In particular, bringing pieces of literature to bear and identifying their limitations according to technological solutions has suffered from insufficient consideration in smart building research. This study also helps identify major trends as well as issues with existing technologies. Thermal comfort, for example, is difficult to define because of the wide range of personal and environmental factors that need to be accounted for. That is why many smart building technology control systems designed mainly for commercial buildings produce poor results when deployed in residential building types. It is also noted that the security and privacy considerations are ignored in many smart building technologies. Similarly, this study finds that the developments of scalability and interoperability solutions among smart building products have increased in recent years. This indicates that smart building technologies are moving towards a new maturity level. However, to date, smart building controls are limited to the number of devices they can accommodate, with a lack of universal protocols to enable different smart building products to recognize, communicate, and share resources in cooperative manners regarding their brands.
Thirdly, the present study makes recommendations for future research to improve the functionality and application of SBEMS. Some of the important dimensions for future work are discussed below.
This study has introduced a different literature classification, given an in-depth analysis, and an approach focused on exploring strengths and weaknesses. Our analysis could be expanded in an empirical study rather than being analytical.
Our research offers an opportunity to test and validate the ideas and constructs that have arisen from our findings. The concept of personal comfort, for example, would need more refinement and elaboration in terms of both its component elements and internal dynamics. For example, our findings reveal there is variation of personal comfort in terms of age level, and different types of settings in which people spend their lives (rural, urban and suburban environments). SBEMS may also be explored to optimize the thermal comfort range for occupants based upon their behavior and preferences.
The research could be expanded in both longitudinal and comparative contexts. For example, in this case, we hypothesized the current solution's output efficiency in terms of thermal comfort and energy savings potentials. More research may enrich this aspect, providing helpful information for choosing the best algorithms and data sets. Further analysis may take an evolutionary approach, asking whether specific algorithms or methods have dramatically shifted in the last decade in terms of efficiencies, which would help those charged with selecting or designing realistic control solutions.

Conclusions
This study reviewed SBEMS literature covering different types of smart technologiessuch as smart HVAC systems, smart lighting, smart plug loads, smart window systems, smart energy optimization system, human operation, and distributed energy resourcesin order to identify not only the current trends, challenges and opportunities but also the future research opportunities to improve buildings energy performance. This study consisted of a systematic literature review across five major databases, "Scopus", "Elsevier", "IEEE Explore", "Science Direct", and, "Google Scholar" from the year 2010 to 2020. The search process used four main keywords: "IoT Energy", "SmartHome", "Smart Home Energy Management System", and "Indoor Comfort Control". The literature reviews initially identified 1127 studies as relevant. After applying the selected inclusion and exclusion criteria, the total number of literature investigated was reduced to 188. Out of the 188 examined pieces of literature, 64% scored 4/4 in the quality assessment evaluation (QAE) process. The analysis also showed that 43% of the literature was focused on the implementation of SBEMS algorithms, while the remaining 57% dealt with other areas such as architecture, framework, and models or platforms. The study classified the literature according to the level of automation strategy employed to manage appliances' operation and energy consumption. Analysis reveals 95% of the literature implements a predictive approach, with more than 65% targeting commercial buildings. The result shows that more than half of the literature focused on integrating SBEMS in commercial buildings using occupant schedule activities. The finding shows that existing literature pays more attention to HVAC systems' energy consumption with less emphasis on smart lighting solutions. The result further reveals that smart building technologies' improving performance efficiency has received attention more than any quality attribute. Most smart building technologies use sensors based monitoring to analyze various building parameters and uses actuators to perform tasks, often in real-time, towards accomplishing a convenient smart building experience. Such a sensing and actuating mechanism is usually quite time-sensitive. To enhance smart building efficiencies, instead of focusing on what sensors and automation can do by themselves, the researchers should also look into how technology such as machine learning can be incorporated to enhance collaboration between smart buildings and occupants through effective interactions. Smart buildings are a perfect example of Big Data incorporating details about occupant data and behavior. The security and privacy of data is an area that needs to be better addressed. The findings also reveal that smart building technologies are getting ever more digitalized, from connectivity to network protocols. Therefore, there is a pressing need to address scalability and interoperability issues, which are not adequately covered in the existing literature.