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Review

An Overview of State-of-the-Art Research on Smart Building Systems

by
S. M. Mahfuz Alam
1 and
Mohd. Hasan Ali
2,*
1
Department of EEE, Dhaka University of Engineering & Technology (DUET), Gazipur 1707, Bangladesh
2
Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USA
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(13), 2602; https://doi.org/10.3390/electronics14132602 (registering DOI)
Submission received: 11 April 2025 / Revised: 5 June 2025 / Accepted: 18 June 2025 / Published: 27 June 2025
(This article belongs to the Section Industrial Electronics)
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Abstract

Smart buildings require an energy management system that can meet inhabitants’ demands with a reduced amount of energy consumed by the heating ventilation and air-conditioning system (HVAC), as well as the lighting and shading systems. This work provides a detailed review of available methods proposed in the literature for effective control of automated systems such as HVAC, lighting, shading, etc. Moreover, effective forecasting of renewable energy generations and loads, scheduling of loads, and efficient operations of thermal and electric energy storage are crucial elements for energy management systems for ensuring reliability and stability. In this work, these aspects of energy management systems, that have been popular over the last ten years, are analyzed. In addition, the development of internet-of-things (IoT)-based sensors widens the artificial intelligence (AI) and machine learning applications in smart buildings. However, this system can be vulnerable against cyber-attacks. The state of the art of AI and machine learning applications along with cyber security issues and solutions for smart building systems are discussed. Finally, some recommendations for future research trends and directions on smart building systems are provided. This work will provide a basic guideline and will also be very useful to researchers in the area of smart building systems in the future.

1. Introduction

The increase in the consumption of electrical energy in buildings and the market price of energy have issued a major concern among engineers, researchers, etc. [1,2]. Therefore, researchers have been extensively investigating smart building or net-zero energy building technology, as almost 40% of the total energy and 70% of electricity produced in the USA is consumed by commercial, office and apartment buildings [3,4]. Smart buildings are supposed to be equipped with automated systems such as lighting, shading, heating, ventilation and air-conditioning (HVAC) systems, etc., for fulfilling the consumers’ comfort demand [5,6]. Another feature of smart buildings is their capability to maintain two-way communication between the service provider and the buildings to facilitate load scheduling, comfort demand based on market price, energy available, etc. [7,8]. With technological advancement, new features for lighting, shading, HVAC, etc., are being introduced to provide more comfort to the inhabitants. These improved features not only make these systems more complicated to handle, but also consume more energy [9]. The increased energy consumption creates huge demands on utility service providers, especially during peak hours. The building energy management system (BEMS) is the backbone control system that coordinates and controls automated systems with the power available from grids and renewable energy sources. The main purpose of the BEMS is to ensure the optimized and efficient output from the automated system so that the inhabitants can have their desired level of comfort by minimum consumption of electrical energy [10,11,12]. In addition, during peak hours, the cost of energy becomes high. Moreover, the building should be capable of providing consumers comfort demand in case of discontinuation of the power supply from grids, blackout or other emergency conditions.
The net-zero energy building is one that has energy generation equal to the load demand and, hence, the cost of energy reduces to zero. The inclusion of renewable energy resources such as wind, solar, etc., along with plug-in hybrid electric vehicles (PHEVs), not only can provide the required power to the load but also can save the cost of the energy during peak hours [13,14,15]. However, optimal design of renewable energy sources is the key to having satisfactory performance [16]. Moreover, the stochastic and intermittent nature of renewable energy sources requires forecasting methods and additional control systems for ensuring the power quality, reliability, stability, etc., of the supplied energy in residential buildings [17]. Moreover, the integration of renewable energy resources makes the building systems more complicated, with lots of conversion steps before the energy can be used for loads. For example, the output of the photovoltaic (PV) is applied to the dc-dc converter, and then, it is converted into ac voltages and currents by an inverter. Similarly, the output of the wind energy (ac in nature) is converted into dc voltages and currents by the ac-dc converter and then these signals are again converted into ac quantities by inverters. A lot of research has been found proposing converters with less harmonics to make the equipment less heated, ensuring power quality as well [18]. In addition, the loads that are used in smart buildings are both ac and dc types in nature. Designing buses for these loads is another issue of concern. Therefore, the smart building should have an effective hybrid grid system to provide power to both ac and dc loads from ac and dc micro-grids and thus improve the efficiency of the power system by reducing the conversion systems [19,20].
However, the issues of power quality, reliability, etc., that are originated by the intermittent nature of the renewable energy sources, can be solved by an effective energy storage system and the scheduling of loads in the smart building [21]. The energy storage system can store the energy when the produced energy is higher than the load demand. In addition, the stored energy can be used for the load when the production of energy is less than the demand. Thus, the energy storage system, in addition, helps the smart building operate in both grid-connected or stand-alone conditions. Therefore, the combination of smart building and PHEV as distributed energy storage is said to be promising in some literature in providing consumers comfort with a reduced cost of energy [22,23]. The PHEV has battery energy storage which can be charged from the grid and easily used during peak hours. Moreover, battery energy storage and thermal energy storage have been considered in most literature due to some inherent advantages discussed later in the energy storage section [24,25]. However, the investigation of performance of other energy sources or combination of energy storage systems for smart buildings is still to be addressed. Moreover, the scheduling of loads adds huge flexibility in the building energy management system that is crucial for smart net-zero buildings. The summary of the abovementioned literature is provided in Table 1 below.
The sensors and internet protocols control the operations of most of the automated systems present in smart buildings. These comfort devices can be remotely controlled due to advancement of IoT technology. The data obtained from smart sensors and appliances managed by IoT technology can be analyzed by AI and machine learning algorithms for monitoring, effective operations, etc. Moreover, the outputs of renewable energy sources and energy storage are controlled by a controller that can be accessed remotely using the internet. Therefore, the whole energy management system needs to be robust against cyber-attacks.
There are a few review papers available on smart residential buildings. Among them, the key features of smartness in residential buildings are analyzed in [26]. An overview on modeling, designing and optimizing energy systems in residential buildings is provided in [1]. However, the purpose of this paper is to investigate cutting-edge technologies on different facets of smart buildings such as smart operations of automated energy management systems, energy storage systems, forecasting renewable generations and loads, load scheduling, AI and machine learning applications on different facets of BEMS, cyber-attacks and their prevention, etc. To the best of our knowledge, it is the most updated and inclusive overview paper on smart buildings that will provide a basic guideline for researchers in the area of smart building systems.
This paper is organized as follows. In Section 2, the concept of smart building energy management is provided, and also, a detailed literature review on various aspects of smart energy management systems of buildings is presented. The AI and machine learning applications in smart building systems are presented in Section 3. Cyber security issues and solutions are described in Section 4. Recommendations for future works are provided in Section 5. Finally, a conclusion is drawn in Section 6.

2. Smart Building Energy Management System

A smart building is equipped with renewable energy sources such as solar and wind power, along with energy storage systems. All the components consuming energy such as lighting, shading and HVAC systems should be operated by smart sensors that can be controlled manually or remotely. The data from all the energy sources, energy storage and loads are stored in a centralized controller commonly known as BMES. Figure 1 shows the bi-directional communications maintained at smart buildings among all the components and BEMS. Moreover, due to smart operations of appliances consumers visual, thermal comfort is maintained with reduced consumption of energy, which increases financial savings and electrical efficiency of the system. Moreover, in a smart building, the consumers’ behavior under different atmospheric conditions is tracked so that all the appliances’ operations can be controlled accordingly.
The smart BMES is the control system that ensures the efficient performances of automated systems based on the conditions, such as inhabitants’ desire for comfort level, pricing of the grid power, climate change, etc. In addition, BEMS provides the required power for the automated systems from the available energy resources (i.e., grid power, renewable energy sources and energy storage). The basic block diagram of the components of BMES is shown in Figure 2. The components of BEMS are (i) automated systems for providing inhabitants’ demand, (ii) energy sources for providing power to automated systems and even to the grid if possible and (iii) energy storage to keep balance between the production and demand power.
An effective BEMS requires an efficient scheduling scheme for proper balance between available energy sources and load demands. Further, it requires a forecasting model to have knowledge of renewable generation and loads ahead of time so that it can develop a strategy for managing the demand with the available energy resources. BEMS can reduce the burden on the grid by suitable scheduling of load or usage of the renewable energy sources during peak hours and thus can save a lot of cost of energy. Therefore, load scheduling or altering loads based on climate change, electricity market price and consumer comfort demand has been the advanced feature of BMES. Sometimes, utility service providers offer incentives to encourage the consumers to schedule schedulable loads such as washing machines, electric vehicles (EVs), etc., during peak hours to off-peak hours. Therefore, participating in demand side response, the consumer can not only earn money but also receive electricity at a reduced price during off-peak hours. As a result, load scheduling has been very popular and different methods on load scheduling have been considered in the literature over the last 10 to 15 years. In any load scheduling method, objective function is first developed based on criteria such as consumers’ comfort demand, reduction in energy cost, taking the least amount of power from the grid, total harmonic reduction, battery degradation cost, etc. This objective function must be optimized to obtain the desired outcome while scheduling the loads. Sometimes, two or more objectives are optimized while scheduling the loads. Moreover, providing power for EV charging has created great challenges for large residential buildings during peak hours. Therefore, a lot of research has been conducted for EV charging scheduling as well. In [27], the authors proposed a two-layer optimization method for efficient electric vehicle scheduling for a smart building. Mixed-integer linear programing (MILP) was proposed for controlling controllable thermal loads, battery energy storage and building integrated with a photovoltaic-based energy management system in [28]. Similarly, MILP is also proposed to integrate multiple buildings with solar power into the distribution network to reduce the overall energy cost [29]. In [30], considering real-time energy pricing, a multi-objective, multi-layered hierarchical load management system is proposed to reduce peak load demand, electricity cost and consumer discomfort. The authors in [31] proposed offline scheduling algorithm for shifting the loads during the off-peak hours and utilizing renewable energy sources during peak hours to reduce the consumption cost of energy. A greedy algorithm was proposed for a smart building to give priority to a renewable energy source for operation in [32]. In addition, a neural-network-based optimizing scheduling model was used to facilitate the greedy algorithm implementation. Similarly, a model-predictive control (MPC) technique was proposed for scheduling power flow among smart buildings in [33]. The authors in [34] proposed a quality of excellence (QoE) model for load scheduling to keep the load under a certain limit to prevent blackout when connected to the grid system. The authors in [35] proposed a genetic algorithm-based intelligent residential energy management system (IREMS) to shift the scheduling load while providing power to the working loads with renewable energy sources and battery energy storage with the aim of reducing the cost of energy while maintaining the desired comfort level of the consumers. It also claimed to investigate several aspects, such as the power exporting option from the battery to the grids, effective sizing of renewable energy sources and energy storage, etc. The authors in [36] reviewed different scheduling methods for load, renewable energy and hybrid energy storage systems with the aim of minimizing the energy cost and improving the energy utilization of the buildings. In [37], a constraint programming algorithm is proposed for optimal scheduling of EV power demand with the aim of reducing peak-to-average power ratio, increasing consumer comfort and suppressing peak load after charging interval. A non-convex mixed-integer nonlinear problem is introduced to regulate energy consumption to reduce energy cost in [38]. A particle swarm optimization (PSO)-based day-ahead hydrogen and battery energy storage scheduling is proposed in [39]. An efficient thermostatic load (HVAC, heat pumps, water heater, refrigerator, etc.) scheduling algorithm is developed to reduce carbon emission in [40]. Similarly, a tri-level aggregated HVAC and EV scheduling is proposed to minimize aggregator energy purchasing cost while ensuring consumer comfort in [41]. It also ensures minimal generation cost satisfying all constraints and optimal dispatch for the load demand on the consumer’s end. In [42], a multi-objective smart home appliances’ scheduling algorithm is proposed and analyzed with four optimization algorithms under different operating conditions for maximizing user comfort, minimizing electricity bills, etc. In this work, user comfort is modeled in terms of delay time, as users have to sacrifice their comfort if some of the demand is fulfilled later. Similar work is also found for scheduling, considering residential buildings as virtual energy storage [43]. MILP is also used for reducing energy cost for the consumer while maintaining the desired comfort level in [44]. A scheduling algorithm to reduce overall operating costs is proposed in [45]. A brief summary of the abovementioned scheduling algorithm proposed in the literature is provided in Table 2.
However, the effective forecasting of climate change, renewable energy output, electricity price or the load demand play a significant role in making the BEMS efficient. The renewable energy output and residential load demand can be very intermittent in nature. If the renewable and load forecasting methods lack accuracy, it will create problems for the energy management system, as it makes a strategy based on it. Although many forecasting methods have been proposed in the literature, new forecasting methods are being proposed that can predict the intermittent nature of the solar and wind energy and consumer demand that can vary over a wide range based on atmospheric conditions and other attributes. Therefore, the authors in [46] proposed a multipoint Fuzzy prediction method for load forecasting to facilitate the energy management system to optimize the power from energy source and energy storage. The authors in [47] proposed a forecasting algorithm for both production of power and consumption of load for the smart operation of the office building management system. The optimization is achieved by comparing the forecast data with actual data. Moreover, in the literature, a review paper on data acquisition techniques is available [48,49]. The authors in [50] analyzed different artificial intelligence-based load forecasting methods in another review paper with the aim of finding the effectiveness of the methods as well as finding the scope of future developments. However, in the last seven to eight years, new forecasting methods have been proposed in the literature. A real-time power output forecasting demand side management (DSM) control of buildings connected to renewable energy sources was proposed in [51]. The authors proposed a Fuzzy logic and subtractive clustering-based ANFIS system for residential load forecasting in two research works [52,53]. In [54], the authors utilized weather and survey data during random forest algorithm training for residential load forecasting. Moreover, the performances of four machine learning algorithms are compared for residential and commercial building load forecasting using historical data in [55]. The Table 3 shows the state of the art of different aspects of forecasting methods for smart residential buildings.
Moreover, a lot of research has been ongoing for selecting models or optimization techniques for BMES connected to renewable energy sources. Most of these optimization approaches or BMES models were proposed to minimize the cost of energy with almost zero compromise on the consumers’ demand. In [56], the Pareto-optimal front algorithm-based multi-objective optimization technique was proposed for energy management systems with the aim of having effective control over the automated systems. The work [57] considered control management for both energy storage and demand side to facilitate micro-grid topology. In this work, KNX (i.e., standard protocol EN 50090)-based control systems were considered for the automated system. In work [58], a multi-agent-based optimization method was proposed to ensure the optimal power flow among the smart buildings in smart grids. This work claimed less transmission loss while delivering power among multiple buildings. In [59], a comparative study is performed among different mathematical and heuristic optimization techniques such as MiniMax algorithm (MM), genetic algorithm (GA), particle swarm optimization (PSO) and quantum particle swarm optimization (Q-PSO) to optimize the energy consumption of HVAC systems targeting room temperature, humidity, mixing ratio and flow rate on a smart office building connected with grid and PV power. Among the techniques analyzed for the prescribed task, the MiniMax algorithm was reported to be the most effective that required less execution time and less iterations while less energy (kWh) was consumed by the HVAC system. The peak demand (kW) was also the lowest for MiniMax optimization. A model-predictive control-based model was proposed for the energy management system for a smart home energized by solar, wind, bio-mas, grid power and energy storage in [60]. In this work, the model was implemented on the household and efficacy of the model was observed with and without the energy storage. A supervisory control and data acquisition system (SCADA)/human machine interface (HMI)-based model was proposed for the building management system in [61]. The authors in [62] proposed a smart home energy management system (SHEMS) concept for controlling the loads both manually by the consumers and by SHEMS based on criteria such as maintaining the communication between power service providers and SHEMS.
A four-layer hierarchical control technique-based energy management system was developed for building an integrated photovoltaic (BIPV) system with dc micro-grid in [63]. In this work, communication between the smart grid and BIPV was considered to facilitate intelligent decision making by management systems in advance for the cases such as grids’ incapability of providing power or injecting the extra power into the grid by BIPV. In [64], the modeling on the Lab Volt home energy management system of a smart home was analyzed. The authors in [65] proposed a simulation-based approach for smart energy management systems that have wind and solar power as energy resources. A global model-based anticipative (GMBA) building energy management system is analyzed with the aim of minimizing the discrepancy between the consumers’ comfort and electricity price considering factors such as consumers’ expectation, power limitation, etc., in [66]. The authors in [67] studied 121 published articles with the aim of giving a future direction on effective optimization and control methods for BEMS. Different data science approaches were investigated to increase the energy efficiency of BMES in [68]. The authors in [69] proposed a multi-objective optimization problem-based game model for BMES clustered with PV systems.
However, the efficiency of the BMES depends upon the efficient operations of automated systems, renewable energy sources, effective communication between grid and smart buildings and efficient operations of energy storage. Therefore, extensive research has been performed on each component of the energy management system, which are discussed in the following three sub-sections.

2.1. Automated Systems

Extensive research has been performed on the effective control and coordination of different comfort features in smart buildings. Researchers have been trying to improve the performance of the HVAC, lighting and heating systems in smart buildings. Some of them have mainly focused on the effective use of these systems so that the cost of energy is reduced. Others have proposed efficient controllers that can optimize the system performance and improve the efficacy of the system. A Fuzzy logic-based efficient automated control scheme for controlling lighting, heating and HVAC systems of a smart office room was proposed in [6]. Based on the inputs such as solar irradiance, inhabitants’ comfort mode, etc., a membership function was developed for the system. The system provides good control features and energy cost is reported to be reduced if the predicted inputs are matched with the actual inputs.
The authors in [70] proposed a hybrid multi-objective genetic algorithm (HMOGA)-based Fuzzy logic controller for the effective control over the automation systems to satisfy the consumers’ demand with lower power utilization. The method used in [70] is more adaptive in nature as compared to the method proposed in [6]. However, the same authors in [70], in their review paper [67], suggested that investigations on more effective agent-based adaptive systems with efficient and intelligent optimization algorithms are still required to be implemented on the building automation system. In addition, they also conclude that a combination of predictive and adaptive models should be used for the prediction of inputs of the controller to make the system more intelligent and time-efficient.
Moreover, a lot of research has been conducted on the improvement of the performance of building automation systems by using automatic control strategies based on European standard EN15232 [71]. The authors investigated different automation systems based on EN15232 in three different building envelopes with the aim of saving the consumption of energy. Similarly, the performance of automation systems based on EN15232 was investigated to save energy in educational institutions in Brazil [72]. The authors made a comparative analysis among the three optimization techniques, such as a first-order approximation of buildings’ thermal behavior, a parameter optimization strategy and a model-predictive controller with the goal of optimizing the performance of an HVAC system on smart buildings [73]. Three models were simulated in thermal simulation to analyze the feasibility. After relative comparison, the authors concluded that although all models were complex, they were proven to be potential candidates for increasing the overall energy.
In [74], an economic model-predictive control technique was proposed for HVAC systems. In this work, the chilled water thermal storage was considered with the HVAC system, and a case study was investigated on the thermal façade (i.e., the interface between the outdoor weather and indoor demand) lab. The performance of the proposed technique in terms of computational time and control proved to be satisfactory. Model-predictive control (MPC) combined with weather prediction was proposed for HVAC, lighting and blind systems to meet consumers’ demand while reducing the cost of energy [75]. In this work [75], the performance of MPC with simple weather prediction was compared with that of with actual weather prediction to validate the importance of the effective weather prediction. Moreover, the authors in [76] proposed the MPC strategy for optimal operation of the HVAC system. In the proposed work, the Nonlinear Autoregressive Neural Network (NARNET) and mixed-integer nonlinear programming (MINLP) were considered for thermal behavior modeling and optimal control problem formulation, respectively. In [77], a multi-agent-based management system was proposed to monitor and control the automated systems. In this work, three different agents are considered and optimized performance for the consumers’ comfort is achieved with reduced cost of energy.
A decentralized control based on the tool from the Game theory and multi-agent learning was proposed to co-ordinate and control the heating, ventilation and air-conditioning unit in [78]. The purpose of this work [78] was to control the temperature within the desired range while reducing the cost of energy, and the efficacy of the proposed system was validated by the simulation results. Moreover, decentralized consensus-based control was proposed with the aim of reducing the cost by setting an optimal reference temperature point closer to each occupant’s preference in [79]. The authors in [80] investigated the performances of the Fuzzy, Adaptive Neuro-Fuzzy Inference System (ANFIS)-based and Artificial Neural Network (ANN)-based control for controlling indoor room temperature and thermal comfort. Among the studied methods, the ANFIS and ANN were claimed to be adaptive and efficient in providing stable thermal comfort such as air temperature with improved stability and less deviation from the set point. However, none of them was found to be effective in energy savings. In [81], a stochastic dynamic programming (SDP)-based control of air-conditioning systems was proposed to provide thermal comfort.
Moreover, the lighting plays an important role in efficiency improvement of the automated system and consumes 19% of the total energy consumed in the building. The energy consumption by lighting can be reduced by
  • Using no light for unoccupied places in the building;
  • Using a type of light that consumes less energy;
  • Effective controlling of lighting intensity based on daylight and shading control.
Therefore, a lot of research has been conducted on various aspects such as designing and modelling of lighting systems, reduction in energy consumption, types of lighting used in the building, maximizing daylight, etc. In [82], the authors proposed simulation tools for various prototypes to investigate the energy saving and lighting in the building. An adaptive criterion was proposed for designing a lighting system to reduce the energy comfort while maintaining the consumers’ comfort [83]. The performance of the Fuzzy logic controller was evaluated as a lighting and HVAC controller in [84]. The combination of dimming and task lighting performance was investigated in [85] and the author claimed to have a 59% increase in energy saving as compared to general control. The authors in [86] analyzed the performance of different types of light used for indoor lighting such as an ancient incandescent lamp to present organic light-emitting diodes (OLEDs) with the aim of analyzing their contribution in energy savings. Moreover, a combination of power and beam angle control was proposed and was compared with conventional lighting control in [87]. In the review work [88], the authors studied research trends in lighting technology. However, focus on the daylight-assisted room concept has been increasing over the traditional overhead light-assisted concept, and in recent times, a lot of simulation-based research has been conducted on this concept [89]. The authors in [90] compared the performance of Fuzzy controlled lighting-based room energy consumption with a traditional reference room and found that electricity demand was reduced by 32% using the Fuzzy logic controller for lighting. In [91], simulation-based research was reported on the performance on the various types of lighting controllers and effects of different levels of dimming, switching on/off, etc., in reducing the energy consumption in an office building in hot climate. A new dimming control based on daylight and occupant number variation was formulated in [92] and similar work with distributed lighting sensors and actuators was conducted in [93,94]. A Wi-Fi-controlled system for lighting based on occupancy was proposed in [95]. The authors made a comparative analysis among different strategies to maximize the daylight in smart buildings in [96].
Moreover, the shading system, by increasing the visual comfort, reduces the energy consumption of the lighting systems [97]. During hot days, when the daylight amount is high, the shading should be maximum to give visual and thermal comfort but it should be reversed during cold days. Therefore, the significance of shading control for visual comfort and energy comfort was investigated in [98]. Moreover, a shading strategy during cold climate was proposed in [99]. Human interaction with shading and lighting was proposed in [100]. An effective model for shading and lighting was proposed in [101]. The advanced shading control should take the daylight amount as input to maintain the visual comfort of the occupant. Therefore, a daylight link synchronized model and a new daylight glare evaluation model was proposed in [102,103], respectively.

2.2. Energy Sources

The design of micro-grid systems is very crucial for providing power to loads, maintaining power quality and stability. The authors in [104] proposed a hybrid energy system that consists of power grid, solar energy, wind energy and fuel cell for a smart building. The proposed system claims to have better performance such as less voltage and frequency fluctuation, as well as less harmonic distortion over the conventional system with the inverters. However, the system is reported to have less efficiency and a high cost as compared to the conventional system. To avoid harmonics and conversion loss, separate dc and ac grids for dc and ac loads, respectively, were suggested in [105]. Moreover, an optimized control method was proposed in [105] to facilitate both stand-alone and grid-connected conditions. A smart meter-based dc/ac micro-grid was proposed in [106] with the aim of reducing harmonics and improving power quality of the system. The author proposed a hybrid micro-grid-based smart power system for the building in [107]. A dc micro-grid system powered by solar energy and battery energy storage was proposed in [108,109]. A low-voltage dc nano-grid was proposed in [110] to incorporate all the energy sources (wind, solar) and energy storage (battery, ultra-capacitor, fuel cell stacked with electrolizer) with the aim of reducing the energy consumption of cooling systems while maintaining the thermal comfort and optimizing the power management system by MPC. The droop control method was proposed in [111,112] for solar, wind, battery and hybrid electric vehicle-integrated dc micro-grid with a distributed droop controller in achieving net-zero energy consumptions. The main objective of this work was to evaluate the effectiveness of the distributed droop controller in effectively managing the surplus power from renewables to charge the battery, which can be utilized for loads later, keeping the dc bus voltage to permissible limit, which was evident from the simulation results of power exchange between renewable sources and load, and voltage of the distribution bus and state of charge (SoC) of the battery. The authors in [113] proposed a single-phase reconfigurable inverter capable of dc-dc, dc/ac and grid-tied types of operation. This inverter was claimed to be useful for houses energized by solar energy. Because of its three modes of operation, it was capable of increasing the reliability of the system by providing dc power directly to the dc loads without any ac-dc conversion and, thus, it reduced the harmonics of voltages and currents in the system. Similarly, the dc-dc, dc-ac and ac-dc conversions were performed by a solid-state transformer (SST) to provide power for loads through hybrid micro-grids [114]. In this work [114], the proposed SST was implemented on a test bed with the aim of testing the performance of the home energy management system in controlling appliances on hybrid micro-grid-based smart homes. Moreover, a combination of fuel cell and ultra-capacitor systems, connected to hybrid micro-grid, was proposed as an efficient solution to provide power to the residential loads during islanded operation [115].
Scheduling and utilizing all the loads effectively require optimal power flow from the grid during off-peak hours when the energy price is low. However, during peak hours, surplus energy can be supplied to the grid when the price of energy remains high. Therefore, infrastructures and effective controllers for bi-directional power flow have been the key features for smart grid and smart home systems. In [116], the economic dispatch model of power systems was incorporated with the thermal dynamics of the building and end-use constraints to formulate the effective building-to-grid (B2G) model. In this work, the smart electric thermal storage (SETS) devices were chosen over thermal energy storage devices such as SETS space heater and SETS water cylinders. The authors in [117] proposed a multi-agent-based control for optimal bi-directional power flow between smart grid and buildings.

2.3. Energy Storage System

The energy storage system plays a pivotal role in maintaining the stability and the reliability of the building’s power system. It also reduces the loads on the smart grid during peak hours by supplying power to the loads provided that the power coming from the renewable energy sources is not sufficient to meet the demands of. Therefore, lots of research has been performed on energy storage systems, mainly on battery energy storage (BES) and thermal energy storage (i.e., ice storage, hot water tank and heat pump).
Due to its low cost, the battery energy storage was proposed by lots of researchers for the smart building [118,119,120]. However, the battery storage has some inherent disadvantages such as low power rating, low life cycle, sluggish response, etc. Therefore, research needs to be conducted on the fast charging and discharging of the battery system. Moreover, the ac and dc loads are available in the building. Therefore, the battery energy storage should have the capability to provide both active and reactive power to the loads during peak hours or islanded operation. Currently, a lot of research is going on to facilitate battery energy storage with real and reactive power delivery capability to power systems. The authors in [121] proposed a voltage source converter (VSC) connected with battery energy storage and PV array. The VSC can provide reactive power to the loads by appropriate control. A similar approach was considered in [122]. The authors in [123] considered a symmetrical component-based static synchronous compensator (STATCOM) and battery energy storage combination for providing real and reactive power to the distribution line. In [124], a sliding mode-controlled STATCOM was proposed with battery storage to provide power to different loads connected to micro-grids. The same authors also proposed a Fuzzy logic-controlled STATCOM in [125]. A cascaded H-bridge multilevel converter was proposed in [126], where a vector control method was utilized to control active and reactive power to and from the converter. To the best of our knowledge, no attempt has been made on VSC-connected battery energy storage for smart buildings. Therefore, further research can be conducted on this with effective control algorithms.
A lot of research has been carried out on thermal energy storage as well [127,128,129,130,131,132,133]. The authors in [134] compared the performance of battery storage, ice storage and hot water tanks. The stochastic optimization method is implemented for scheduling the usage of available energy resources for different operating conditions. A water-cooling tank was proposed as a thermal energy storage system for effective operation of energy management to reduce energy consumption [135].
However, the combination of high-power density energy storage with high-energy density storage devices can be utilized as a protection against high fluctuating loads or renewable energy storage. The example of high-power density energy storage systems includes the super magnetic energy storage system (SMES), supercapacitor energy storage system, etc. Moreover, the battery is a high-energy density storage device. These types of hybrid energy storage have been very popular over the last decade. The authors in [136] proposed a battery and supercapacitor-based energy storage system for a smart building energized by diesel generator and renewable energy sources. Similarly, supercapacitor and battery energy were proposed as a hybrid energy storage system for an islanded smart building powered by solar energy [137]. Similarly, a hardware-in-loop-based simulation system was proposed to investigate the performance of a smart home with the presence of an energy management system and an energy storage system in [138]. This work also considered supercapacitor–battery-based hybrid energy storage. Despite this research, there is a lot of scope of improvement in the performance of battery–supercapacitor-based energy storage through adaptive and efficient nonlinear controls.
A detailed comparison on different features of different SMES systems was presented in [139]. This work can be considered as a seminal work on the SMES and provides guidelines for further technological development for these systems. The performance of an SMES-based storage system, with efficient adaptive and nonlinear control and distributed generation in smart building, can be considered as a further research option as it can control both real and reactive power flow to or from the energy storage system and enhance the reliability of the system by its fast adaptive response [140,141,142,143,144,145,146,147,148,149]. Moreover, the SMES battery-based hybrid storage can be considered for future smart buildings, since this has a variety of applications in power systems [150,151].
The authors in [152] investigated the performance of the supercapacitor energy storage for wind-based distributed power systems. The proposed supercapacitor energy storage system performs better compared to crowbar resistors or derated converter-based systems. It also has the ability to provide reactive power during the faults. Similar results were observed in [153], where a Fuzzy control-based super capacitor energy storage system was analyzed for faulted condition. Like the SMES system, the super capacitor energy storage is a high-power density energy storage with fast charging and discharging capabilities. Moreover, it has real and reactive controllability [154,155,156,157,158,159,160]. Therefore, a lot of research has been directed toward supercapacitor energy storage for micro-grid-based power systems. The supercapacitor energy storage can be analyzed as an option for energy storage for smart building systems, although one work [123] is available. In addition, the battery and supercapacitor-based energy storage has been applied for different applications [161,162,163].

3. AI and Machine Learning Applications in Smart Building Systems

In recent years, due to the advancement of data collection and sensing technologies, more accurate and real-time data of energy usage, indoor temperature, humidity, lighting levels and occupant movement, etc., are available for smart buildings that can be used for controlling IoT devices to efficiently use energy while maintaining consumers’ comfort [164]. Therefore, researchers are working on how artificial intelligence (AI) and machine learning can be applied for automation, load forecasting, scheduling and other applications in smart residential buildings.
The combination of IoT, AI and Android App was proposed for observing the present status and controlling electrical appliances like lights, fans, smoke, gas and fire detectors and sending alerts on the user’s Android Application to take precaution remotely in [165]. Explainable ML models based on edge computing for building energy monitoring and management were proposed in another work [166]. A mobile application is developed for fast and remote monitoring, tracking and energy consumption management. In [167], the importance of using AI-based tools to predict and analyze energy-related data required for improved efficiency and reliability of the energy system was analyzed. Current technologies with their applications, benefits and drawbacks are compared and promising technology that can be used in the future is discussed.
Moreover, an efficient load forecasting model will be a key element in future smart buildings. Due to improvements in sensors and IoT devices, lots of energy consumption data can be stored, which can further be used by both AI and machine learning methods for residential load forecasting [46,47,48,49,50,51,52,53,54,55]. Therefore, forecasting using machine learning and AI have been very popular in the last five years. A Bayesian neural network (BNN)-based short term load forecasting method was proposed for PV-integrated residential buildings in Cyprus [168]. The proposed algorithm is applied to data obtained from 68 individual and aggregated data and performs well for the aggregated data. In three works, very short-term load forecasting and HVAC power consumption forecasting based on deep neural networks are proposed, respectively, in [169,170,171]. In another work, Long Short-Term Memory (LSTM) is claimed to have performed well among machine learning methods for predicting heating and cooling loads for summer and winter [172]. The performances of random forest (RF), XGBoost, LSTM and GRU are analyzed for predicting hour-ahead and day-ahead lighting, socket power and air-conditioner load forecasting for residential buildings in Vietnam [173]. In the case of lighting power forecasting, all the methods perform well, as lighting loads are relatively repetitive. As the socker power varies over occupant behavior, day–night cycles and other variables affect the performance of GRU and LSTM decreases with the increase in time horizon due to increase in uncertainty. An almost similar scenario is observed for air-conditioners’ load forecasting. A multi-layer neural network and an LSTM-based sequence-to-sequence model are proposed in [174,175], respectively. In another work, a deep neural network-based transfer and a meta learning model are proposed in [176]. EXplainable Artificial Intelligence (XAI) is introduced for load forecasting in [177]. For DC load forecasting, an extreme XGBoost model is introduced in [178].
In addition, many researchers have proposed hybrid models to improve the residential load forecasting accuracy in recent years. A CNN-BiLSTM-SA-based forecasting model is proposed in [179]. In another work, a hybrid CNN-LSTM method is proposed for electric vehicle demand and load forecasting for residential buildings in [180,181], respectively. SARIMA-SVR- and SARIMA-ANN-based hot water demand forecasting is introduced in [182]. In this work, six months of electric boiler data are collected and categorized into hourly and daily data and SARIMA-ANN outperforms SARIMA-SVR for both hourly and daily data predictions. This result indicates that the ANN hidden layer effectively learns and captures the residue that SARIMA cannot capture. Moreover, the performance of SVR depends on the hyperplane. In another work, LSTM–Autoencoder Network for residential load forecasting is proposed in [183].

4. Cybersecurity Issue and Solution for Smart Building Systems

With the advancement of science and technology, a smart home without the internet of things (IoT) is unimaginable. By connecting all the components such as HVAC, lighting, security camera, etc., the consumer can monitor and control the appliances at any time from any location [184]. Therefore, research for IoT approaches for various aspects of smart building applications, such as feature improvement of automation [185], energy modeling [186], mobile application [187], device identification [188], finding consumer location (commonly known as micro-location) [189], energy consumption reduction [190] and for non-ADA (Americans with Disability Act) buildings [191], is gaining popularity. In addition, some research articles regarding IoT and big data technology on smart metering [192], energy management system [193,194], renewable energy source integration [195] are found as well. Packetized direct load control techniques were proposed in [196]. In [197], a novel localization method was proposed to facilitate a fingerprint-based model. An intelligent and efficient data communication was proposed for smart building with the aim of facilitating energy efficiency by incorporating clustering and routing among the sensor-based network.
However, being connected to the internet, the components of BMES are vulnerable to cyber-attacks [198]. Therefore, cyber security in smart building systems should be robust [199] and advanced research is required for cyber security issues such as attack detection, vulnerability and protection technologies, etc., for the smart house components. The authors proposed an anomaly behavior detection system with Zigbee protocol in [200]. Regarding safety and security issues of building automation systems, some approaches are discussed for further development in [201]. An anomaly-based intrusion detection system (IDS) combined with context awareness and cyber DNA technique was proposed as an automation system in [202]. In [203], the effect of a fault or abnormal situation was investigated by a knowledge-based approach. In [204], security issues were analyzed with the aim of finding any gap in security research for cyber-attacks. In [205], two security methods named authenticating mechanism and data privacy detection protection were proposed. The authors in [206] analyzed both content-based and contextual-based privacy methods for smart homes. A common approach was presented to make both safety and security services along with services such as HVAC, lighting, shading, etc., for smart homes in [207]. Both security threats and challenges were investigated, and also, a concept for providing security to smart buildings was discussed in [208]. Table 4 summarizes the literature survey on cybersecurity issues with smart residential buildings.

5. Recommendation for Future Work

Based on the reviews, some recommendations can be made for future research on smart building systems.
(i)
The BEMS plays a pivotal role in providing consumers’ comfort demand with a minimal cost of energy. Therefore, research on effective BEMS is crucial. Although different control algorithms have been proposed in the literature, there is still room for future improvements. In the future, hybrid modulated model-predictive control (HMMPC), Markov decision process and particle swarm optimization (PSO)-based BEMS systems can be implemented to have better performance, as they can capture the intermittent nature of renewable generation and load demands. Moreover, only one publication on Game theory-based HVAC system was published to the best of our knowledge.
(ii)
There is lots of research available for load forecasting of residential loads. However, only limited research on PV power forecasting for residential buildings with no wind power forecasting methods is available in the literature. As the proper operation of BEMS depends on efficient very short-term forecasting with small forecasting (15 min interval, 30 min interval) horizon, new forecasting methods that can predict well with limited data can be investigated.
(iii)
A two-directional power flow from grid to building (G2B) and building to grid (B2G) is the key feature of smart grid and smart building concepts. Research on infrastructure design, demand response optimization, optimal bi-directional power flow (G2B, B2G) and state status of thermal storage has been conducted over the last ten years [209,210,211,212,213]. However, these aspects can be further investigated by the Markov decision process and Game theory-based controller and models because of their ability to capture the intermittency nature well.
(iv)
Lighting and shading consume huge amounts of energy in smart buildings. In recent times, a lot of research has been conducted on PV windows to control daylight and shading in the building, reducing the heating and cooling energy demand while generating extra energy [214,215,216,217,218,219,220,221]. Therefore, new material-based PV windows as well as efficient control technology for PV windows can be a new future direction for smart buildings.
(v)
Smart buildings with plug-in hybrid electric vehicles (PHEVs) as distributed energy storage are capable of ensuring power system stability, reliability and increasing efficiency of BMES. Therefore, different charging and discharging algorithms of battery systems in the PHEV have been proposed in the literature [222,223,224,225,226,227,228,229,230,231,232]. However, the charging and discharging time may affect the distributed power generation. Therefore, new fast charging technology for electric vehicles should be investigated. Moreover, efficient EV charging scheduling algorithms can be introduced to minimize the cost while maintaining the power quality.
(vi)
Smart buildings, equipped with intermittent resources like solar and wind energy sources, require very fast energy storage in order to ensure the stability and reliability of the system. The supercapacitor is a high-power density device with both active and reactive power controllability. Therefore, the performance analysis of supercapacitor energy storage, hybrid energy storage (combination of battery and supercapacitor), etc., still needs to be investigated for smart buildings. The performance of the energy storage system with model-predictive control and hybrid modulated model-predictive control-based dc-dc converter can be a strong candidate in case of input power variation, steady state and faulty conditions. Moreover, the extended Kalman filter has been analyzed for the battery energy storage in distribution micro-grids [233,234,235,236,237]. To the best of our knowledge, it has not been used for supercapacitor energy storage or hybrid energy storage for both distribution micro-grid and smart buildings. The improved version of the extended Kalman filter, which is known as the resilient extended Kalman filter, has been proposed in some literature [238,239,240]. Therefore, these two controllers can be used for further improvement of the performance of dc-dc converter-based supercapacitors or hybrid energy storage. Hydrogen energy storage is an available cutting-edge technology. Therefore, the application of hydrogen energy storage must be investigated.
(vii)
The proper sizing of energy storage is crucial for not only ensuring stability and reliability but also increasing the efficacy of performance and saving cost for energy storage. Recently, researchers have been keen to investigate different optimizing methods for proper sizing of battery energy storage in renewable energy-source-connected smart buildings [241,242,243,244,245,246,247]. Therefore, the system performance can be further improved by implementing the optimization technique for battery as well as supercapacitor, or a combination of both, such as particle swarm optimization, model-predictive control, etc.
(viii)
The component of the BEMS system that works on the signals from the sensors or SCADA can be prone to cyber-attacks. Therefore, effective means are required for automated systems, energy sources and energy storage systems to make them more robust against cyber-attacks [234]. To the best of our knowledge, the performance of the controller (MPC) has not been investigated for a cyber-attack situation. However, the resilient extended Kalman filter is reported to have a robust and better performance in case of bad data insertion, sensor failures, etc. Therefore, the combination of REKF and MPC can be an effective tool for improved performance of energy storage in case of any cyber-attack in the buildings, SCADA systems or emergency situations like failure of sensors, data transmission failure, etc. Moreover, the Game theory and Markov decision are reported to be very successful against cyber-attacks for power systems and smart grids [248,249,250,251,252,253,254]. Therefore, it can be a pivotal contributor to make BMES robust against cyber-attacks.
(ix)
The performance of new communication and AI technology [255] must be incorporated in building technology to make the building system smarter and more efficient.
(x)
As the data from renewable energy, loads and buildings can be gathered, these data can be trained for health monitoring to see how energy consumption and other characteristics vary for loads and buildings being healthy and non-healthy. Very little literature has addressed these issues [256,257].

6. Conclusions

The improvement of energy efficiency in smart buildings along with integration of renewable energy resources is crucial to facilitate the concept of smart grid. An effective energy management system is an essential part of smart buildings for smart operations. Smart operations of BEMS require effective forecasting and scheduling algorithms along with smart control of home appliances. In this work, these aspects of energy management systems, that have been popular over the last ten to fifteen years, were analyzed. As for forecasting, the current trend is implementing deep learning for load forecasting. However, very little research has been conducted on renewable energy prediction, which is crucial for BEMS. Moreover, the inclusion of IoT, AI and machine learning technology not only makes the home appliances smart and remotely controllable but also subject to threats of cyber-attacks. Some recommendations on future research directions on smart building systems were provided. An extensive list of up-to-date references was shown. It is hoped that this study will provide a basic guideline for researchers in the area of smart building systems.

Author Contributions

Conceptualization, S.M.M.A. and M.H.A.; resources, S.M.M.A. and M.H.A.; writing, S.M.M.A.; writing—review and editing, M.H.A.; supervision, M.H.A.; project administration, M.H.A.; funding acquisition, M.H.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Operation of all components within smart building.
Figure 1. Operation of all components within smart building.
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Figure 2. Basic block diagram for building energy management system (BEMS).
Figure 2. Basic block diagram for building energy management system (BEMS).
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Table 1. Literature survey of current articles on different aspects of smart residential buildings.
Table 1. Literature survey of current articles on different aspects of smart residential buildings.
Ref No:Publication YearFocusMain Contributions
[1]2022Review Paper
  • Reviews available literature on
    (a)
    Recent advancement in modeling, designing and optimizing energy system for buildings;
    (b)
    Current trends on building grid integration, renewable sources and hybrid electric vehicle integration in smart buildings.
[2]2023Features of intelligent buildings
  • Investigates the features that need to be incorporated to improve the quality of living for the inhabitants.
  • Suggests the obstacles such as lack of development of information technology, the low degree of integration of technology and governance, etc., that need to be overcome in the future to develop a smart community living in smart buildings.
[3]2018Advancement in net-zero buildings
  • Explains different terms and characteristics that are required to differentiate net-zero buildings from conventional buildings.
  • Identifies some common limitations that are presented in the current research and development that require special attention for future NZEB growth.
[4]2016Integration of smart buildings with the grid
  • Explores different ways of integrating smart residential buildings into the grid, ensuring its stability.
  • Identifies technical and non-technical developments that are required in this regard.
[5]2022Energy management
  • Proposes a model-predictive control-based energy management system for a grid-connected residential building.
  • It incorporates different features such as forecasting of generation, load and power and effective utilization of EVs in an energy management system which is claimed to operate effectively under all testing conditions.
[6]2016Control of automation system
  • Proposes a Fuzzy logic-based control system for automation to keep consumer comfort in solar-powered office buildings.
  • A virtual model of a smart office room equipped with dynamic shading, lighting and an air-conditioning control system is considered to evaluate the efficacy of the proposed control system under four different scenarios.
[7]2022Energy management
  • Proposes a genetic algorithm (GA)-based demand side management system with the aim of maximizing consumers’ comfort with minimal electricity cost.
  • The proposed algorithm can make electrical devices turn on for a specific time to provide maximum user satisfaction at the lowest cost.
[8]2016Smart grid and smart buildings
  • Analyzes the existing challenges in the building integration to the smart grids, such as incorporation of smart metering, participating capability in demand response, proper utilization of renewable energy sources and energy storage, etc.
  • Explains the benefits of integrating buildings into grids and their prospects.
[9]2025Automation system
  • Investigates the effectiveness of schedule-based, sensor-based, IoT-enabled control for automation systems for providing consumers’ comfort by proper energy management.
  • Identifies optimal conditions for both energy efficiency and resident well-being. It also highlights the efficacy of IoT integration in HVAC management
[10]2021Energy management
  • Proposes an energy management system for increasing the efficiency of the energy and reducing consumption.
  • Presents technical and economic justification of innovative normalization technology keeping voltage deviation minimal that can be applied to a smart house or a smart city.
[11]2014Automation system
  • Proposes a virtual model of a smart office room equipped with dynamic shading, lighting and air for analyzing consumers’ conform and energy demand.
[12]2024Energy management
  • Proposes an effective energy management system based on user behavior and energy consumption pattern.
  • Analyses the impact of energy management knowledge on energy usage, user behavior, related expenses and environmental effect.
[13]2024Net-zero building
  • Proposes a cost-effective analysis of a net-zero building with PV and wind energy as renewable resources.
  • Based on the appliances’ usage data, the grid power cost is determined for four cities of four environmental conditions in Iran to make net-zero buildings cost-effective.
[14]2023Smart buildings and renewable energy
  • Suggests a model combining the Earth–Air Heat Exchanger (EAHE), the Underwater Tank (UNT) and the Solar Thermal Collector for meeting the heating demand.
  • Simulations of the proposed model are performed by TRNSYS under different conditions and experimental data are used to validate the simulation results.
[15]2023Smart buildings and renewable energy
  • Investigates the impact of air conditioning on energy consumption in a building when it is controlled by a conventional controller considering Malaysian weather.
  • Compares the conventional control system performance over the compressor’s limit range and proposes implementing inverter control for the air-conditioning system.
[16]2023Smart buildings and renewable energy
  • Proposes Markov chain-based model for designing renewable energy resources considering aging and weather effects in NZEB.
  • Provides insight into how energy flows among various components within the buildings.
[17]2024Smart buildings and renewable energy
  • Analyzes forecasting methods, energy efficiency models and renewable integration for optimization and management of residential energy systems.
  • The VOSviewer analysis is used to find 37 keywords that were grouped into clusters to identify research trends and gaps.
[18]2023Loss reduction in residential buildings
  • Proposes a low-power dc-dc converter to reduce the power loss and improve efficiency.
  • The experimental results support the efficacy of the proposed design for low-power alliances.
[19]2024Hybrid micro-grid system in buildings.
  • Proposes a hybrid ac-dc nano-grid (HNG) for minimizing power loss in power distribution in residential buildings.
  • A Pyomo and the Interior Point Optimizer (IPOPT) is used to optimize multi-objective function to reduce sharing errors caused by the droop characteristics of distributed generators.
[20]2023Hybrid micro-grid system in buildings.
  • Proposes a mitigation method for reducing power oscillation created by double frequency and its higher frequency harmonics in the dc grid of residential buildings.
  • The proposed algorithm improves the dc bus power quality just by using a proper adjustment of each inverter voltage phase reference without adding any additional circuit component.
[21]2024Energy management
  • Proposes an effective energy management system consisting of PV, wind, pumped hydropower and battery energy storage to provide thermal demand of buildings.
  • The proposed system is modeled in TRNSYS and is claimed to have 39% improvement in energy self-reliance compared to conventional approaches.
[22]2024Smart building and PHEV
  • Proposes two effective scheduling methods for charging EVs in residential complex considering limited power supply.
  • The proposed online time-allocated EV charging system model is efficient in the selection of the best of the proposed algorithms based on specific charging requirements, such as electricity price fluctuations.
[23]2024Smart building and PHEV
  • Provides an estimating method for calculating the loads of the charging stations, taking into account the charging infrastructure to enable the electrical networks to supply power accurately to the public buildings.
  • Investigates the influence of the load of electric vehicle charging stations on the load of the power supply system of public buildings and finds no requirement for additional capacities for connecting the charging infrastructure to the power supply systems of public buildings due to abundant power reserve at transformer substations.
[24]2022Thermal energy storage
  • Proposes a hybrid thermal electric system for reducing consumers’ bills while maintaining the desired comfort level.
  • Provides a novel co-simulation framework to model home energy management systems in the presence of electric thermal storage systems including heating load modeled by model-predictive control.
[25]2021Energy management
  • Proposes an energy management system for battery-coupled building micro-grids to improve the performance of batteries as a flexible resource.
  • Takes into account battery degradation and real-life operation characteristics derived from measurements at a residential building, which enable the proposed method to estimate the operational cost of the building.
Table 2. State of the art of different aspects of scheduling methods for smart residential buildings.
Table 2. State of the art of different aspects of scheduling methods for smart residential buildings.
Ref.System DescriptionScheduling ComponentCriteria for Objective FunctionMethods of Optimization/Scheduling
[27]Smart residential buildings operating as distribution network connected to grid.Electric vehicle (EV)Peak load shaving, power quality improvement considering charging cost, battery degradation cost and frequency regulation.A two-layer evolution strategy particle swarm optimization (ESPSO) algorithm
[28]Multiple smart homes in a smart building.Controllable thermal loads, battery energy storageMinimization of daily total energy cost.Mixed-integer linear programming approach.
[29]Multiple buildings having solar power and battery energy storage.Battery energy storage charging and dischargingMinimization of the total expenditure of the buildings.Mixed-integer linear programming approach.
[30]Smart building.Home appliances such as fan, lights, HVAC, motor, TVReducing peak load demand, electricity cost, and user discomfort.Intelligent multi-agent-based multi-layered control system.
[31]Smart building with renewable energy sources and battery energy storage.Home appliancesPrioritizing loads based on the conditions to reduce the cost of energy.Algorithm developed to track time-of-use (ToU) electricity charge.
[32]Smart building with renewable energy sources.Home appliancesPrioritizing use of renewable energy and reduction in energy cost.Greedy algorithm is used to choose energy sources and Boltzmann neural network is used for load scheduling.
[33]Multiple smart buildings equipped with renewable energy, micro-CHP, electric vehicle and energy storage.Exchange of power among the buildings and thermal loadsExchange of energy among the buildings taking advantage of flexibility of thermal loads.Model-predictive control (MPC) algorithm.
[34]Smart building.LoadsControl loads to prevent blackout.Quality of experience (QoE) model.
[35]Smart building with renewable energy and energy storage.Schedulable loadsMaximum utilization of renewable energy and scheduling of loads and battery energy storage to reduce electricity bills.Intelligent residential energy management system (IREMS). Genetic algorithm is used for renewable energy and battery energy storage optimal sizing.
[37]Large residential building areas.Electric vehiclesPeak-to-average ratio reduction, maximization of user satisfaction, suppression of peak load after charging interval.Constraint programming (CP) algorithm.
[38]Smart building with fuel cell, battery storage, thermal and electric loads. Electrical loadsScheduling of appliances to reduce the energy cost.Non-convex mixed-integer nonlinear problem (MINLP) with PSO and gradient-based deterministic algorithm for tuning discrete and continuous variable respectively.
[39]Grid-connected smart building with hybrid energy storage.Hydrogen and battery energy storageScheduling of hybrid energy storage to purchase less power from grid.Particle swarm optimization (PSO) algorithm.
[40]Grid-connected smart building with battery energy storage.Thermostatic loadsTo have less carbon emission.Bi-level optimization reformulation strategy.
[41]Residential distribution network with aggregated HVAC and EV.Aggregated EV and HVAC loadsMinimization of energy cost, maximization of comfort, optimal dispatch to meet demand at the consumer end.Karush–Kuhn–Tucker optimality conditions, MILP and strong duality theory.
[42]Smart building with PV, battery and EV.Smart residential appliancesReduction in energy cost, carbon emission, peak-to-average energy ratio (PAR) and maximization of user comfort.Hybrid genetic particle swarmo
ptimization (HGPO), genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA).
[43]Smart buildings, PV system, micro-gas turbines, electric load.Redundant residential micro-gridOperating cost, thermal comfort level, and pollutant emission (PE).Improved non-dominate sorting genetic algorithm II (NSGA-II).
[44]Smart residential buildings with battery energy storage.Residential loads and DER unitsMinimization of the energy cost for the consumer while maintaining the desired comfort level.Mixed-integer linear programming (MILP) optimization.
[45]Solar PV, battery energy storage, Combined Cooling Heating Power System (CCHP).CCHP systemMinimization of the system overall operating cost.Mixed-integer linear programming (MILP) optimization.
Table 3. State of the art of different aspects of forecasting methods for smart residential buildings.
Table 3. State of the art of different aspects of forecasting methods for smart residential buildings.
Ref.Forecasting AreaAttributes/Input Chosen for ForecastingDataset Used for Training/PredictionsMethodsAccuracy/Improvements
[46]LoadDay, time, temperatureAlmost 7 months of hourly dataMultipoint Fuzzy logic systemLess than 90%
[47]PV generation and loadsSunrise and sunset times, a weather condition code and the percentage of sky coverage due to cloudsTime series data of past six daysOpenWeatherMap API and a triple exponential smoothing model provided by open forecast for PV generation and load forecasting, respectivelyHigh
[51]Solar and wind powerTime series dataTwo monthsCascade Forward Back-Propagation (CFBP) algorithm-based neural network--
[52]LoadTemperature and a variable which is determined from occupancy and day type304 days of data for training and 30 days of data for forecastingFuzzy logic systemMore than 90%
[53]LoadTemperature and a variable which is determined from occupancy and day type304 days of data for training and 30 days of data for forecastingSubtractive clustering-based ANFIS systemMore than 90%
[54]LoadWeather and survey dataTwo years of dataRandom forestMore than 25% improvements
[55]LoadHistorical energy consumption dataMore than 200,000 time steps for residential building and 105,408 time-steps for commercial buildingsLong Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Radial Basis Function Network (RBFN) and Multi-Layer Perceptron (MLP)High
Table 4. Literature survey on cyber security issues on smart residential buildings.
Table 4. Literature survey on cyber security issues on smart residential buildings.
Ref No.FocusMain Contributions
[204]Building automation and control system
  • Emphasizes on the safety and system security of both the network nodes and the communication protocols for residential buildings.
[205]Automation system
  • Proposes an intrusion detection system by context awareness and cyber DNA techniques to detect anomaly behavior in case of cyber-attack or any failure.
[206]Automation system
  • Proposes a fault propagation approach for building an automation system which can analyze far-reaching consequences of the fault.
  • Helps build an automation system to prioritize the proper emergency and maintenance actions in case of a fault.
[207]Automation system
  • Provides a detailed survey on several risks, possible attack vectors and resources and knowledge of possible attackers.
[208]Remote healthcare service
  • Provides an inclusive analysis on usable authentication mechanisms and data privacy protection methods to protect sensitive data such as elderly monitoring or remote healthcare in smart buildings.
[209]Wireless sensors
  • Compares the existing privacy preservation techniques used in wireless sensors in smart homes.
[210]Building automation and control system
  • Provides a basis for building automation and control system by combining critical security and safety technology.
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Alam, S.M.M.; Ali, M.H. An Overview of State-of-the-Art Research on Smart Building Systems. Electronics 2025, 14, 2602. https://doi.org/10.3390/electronics14132602

AMA Style

Alam SMM, Ali MH. An Overview of State-of-the-Art Research on Smart Building Systems. Electronics. 2025; 14(13):2602. https://doi.org/10.3390/electronics14132602

Chicago/Turabian Style

Alam, S. M. Mahfuz, and Mohd. Hasan Ali. 2025. "An Overview of State-of-the-Art Research on Smart Building Systems" Electronics 14, no. 13: 2602. https://doi.org/10.3390/electronics14132602

APA Style

Alam, S. M. M., & Ali, M. H. (2025). An Overview of State-of-the-Art Research on Smart Building Systems. Electronics, 14(13), 2602. https://doi.org/10.3390/electronics14132602

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