Smart Energy Management System Research Articles

A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. In order to avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider: i) the complexity of human behavior when interacting with power conservation systems; and ii) realistic home-level power dynamics. As a consequence, this leads to approaches that are i) ineffective due to poor long-term user engagement; and ii) too abstract to be used in real-world settings. In this paper, we propose an auction-theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator (SO) sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine-learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine-learning-based Power Saving Predictions (PSP) algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial-time algorithm called Greedy Ranking Allocation (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

An integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application

Regarding environmental friendliness, low maintenance needs, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) have become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energy flow system. As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when compared to solo energy sources. Additionally, the primary problem interaction applications, including such battery electric vehicles, are the energy storage system. Multiple energy storage technologies, including battery packs, flywheels, super-capacitors and fuel cells, are combined into a HESS due to their complementing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing backup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of power density. HESS needs a smart Energy Management System (EMS) to function properly since it combines the dynamic characteristics of a battery and a SuperCapacitor (SC). The motive of the study is to suggest an actual power management control system to accomplish these objectives. The plan is built using a wavelet transform, deep learning mechanism, and fuzzy logic together. A useful tool for separating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is challenging to immediately apply it in a system, though. Because of this, the traditional optimization-model-based facility energy management system encounters substantial difficulties with online forecast and calculation. To solve this problem, the paper proposes a ML technique dependent on a Long Short-Term Memory (LSTM). The suggested control system structure allows for the separation of the offline and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline stage. As a result, the supercapacitor receives an online calculation and distribution of the high-frequency power requirement. The SOC of the supercapacitor is kept within the appropriate range via fuzzy logic control. To evaluate the efficacy of the suggested energy management control technique, a 70 V battery with 92 V supercapacitor hybrid energy storage devices for hardware platforms have been created.

Optimizing smart building energy management systems through industry 4.0: A response surface methodology approach

Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficient adjustment to internal room parameters and limited optimization based on varying loads. The current study investigates the influence of Industry 4.0 by assessing internal room parameters in an indoor ventilated room conditions for system output. Smart systems like AI and IoT, integrated with sensors, dynamically adjust indoor conditions based on external climate, enhancing HVAC system efficiency. Using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), integrated with AI and IoT sensors, optimally adjust indoor conditions in correlation with external climate, maximizing HVAC system efficiency. The study utilizes input parameters including Dry Bulb Temperature (DBT), Relative Humidity (RH), and Solar Radiation across diverse days to analyze system efficiency. ANN and RSM iteratively optimize system efficiency by training on RH, DBT, and air quality data. They converge on recommending 42% RH, 28 °C DBT, and 25 PM air quality, achieving 0.851 desirability. This approach enhances heat load (75.89%) and ventilation load (69.12%) efficiency significantly. Integration of Industry 4.0 sensors in the HVAC system resulted in a 16% efficiency increase and 15% cost effectiveness in the controlled room compared to prior measurements.

Smart energy management in residential buildings: the impact of knowledge and behavior

A new technology called smart energy management makes use of IoT concepts to enhance energy efficiency and lower waste in structures. The goal of this study is to comprehend how household energy management knowledge affects energy usage, user behavior, related expenses, and environmental effect. Through a survey of 100 valid replies in Palestine, the research model assessed the knowledge and consumption habits of building occupants. Smart PLS software was used to analyze the research model using partial least squares structural equation modeling (PLS-SEM). Using path coefficients and behavior as a mediating variable, the structural model connected the latent variables. The mediation hypotheses were tested using the Preacher and Hayes method, and the indirect effect and confidence intervals were estimated and calculated using bootstrapping. The findings demonstrated that by lowering energy use and enhancing overall building performance, residential buildings that implement smart energy consumption management systems may move toward a more sustainable future. Furthermore, the study found that education and awareness campaigns are necessary to increase residents’ knowledge of these systems to promote energy savings. The results also indicated statistically significant indirect effects, supporting the existence of mediation of the behavior construct. Path coefficient values and P-values were presented to further support the study’s hypotheses. Such smart energy management systems represent an important innovation in building management and can help create more sustainable and efficient buildings.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

Considering Saudi Arabia's strategy entails economic diversification and a reduction in dependency on oil, a change towards sustainable and efficient energy management practices is required. Effective energy management is critical for optimising energy consumption, lowering costs, and reducing the environmental footprint. Traditional energy management systems are frequently built on rule-based or deterministic techniques that fail to address the complexity and uncertainty inherent in home energy usage patterns and user behaviours. Fuzzy logic-based solutions have emerged in recent years, holding enormous promise for domestic energy management. Fuzzy logic excels at dealing with imprecise and uncertain input, making it ideal for modelling human-centric systems such as domestic energy management. This study presents a cutting-edge fuzzy-based smart energy management system adapted to Saudi residential buildings. Using fuzzy logic approaches, the system intends to optimize energy usage, reduce peak demand, and improve energy efficiency. It automatically adjusts energy-consuming devices such as air conditioning, lights, and appliances depending on user preferences, occupancy patterns, as well as real-time energy prices using fuzzy decision-making algorithms. The research not only substantiates the success of this strategy through a comparative study, but it also highlights its prospective benefits for Saudi Arabian residential constructions. It also covers the obstacles and potential research possibilities for adopting fuzzy-based smart energy management systems, exposing a promising road towards a more sustainable and safe energy future. The study's findings underscore the superiority of the comparative study alternative, revealing it as the most effective means to evaluate the fuzzy-based system's performance and unique advantages in relation to other established energy management approaches.

Techno-economic feasibility analysis of a commercial grid-connected photovoltaic plant with battery energy storage-achieving a net zero energy system

Grid connected Photovoltaic (PV) plants with battery energy storage system, are being increasingly utilised worldwide for grid stability and sustainable electricity supplies. In this context, a comprehensive feasibility analysis of a grid connected photovoltaic plant with energy storage, is presented as a case study in India. A novel smart net-zero energy management system is developed to reduce grid and fossil fuel-based backup electricity consumption during power outages and peak load shaving by controlling peak load demand A life cycle cost-benefit and levelized cost of energy (LCoE) analysis, is presented for five optimised photovoltaic plants with battery energy storage systems, with and without net metering regimes. The Solar Labs and PVSyst softwares are used for system planning and energy generation estimation followed by HOMER grid software and Excel sheet-based financial models for system optimisation and cost-benefit analysis. The sensitivity analysis is carried out considering most sensitive parameters to identify the best option. The results found a 200 kWp photovoltaic plant with 250-kWh battery energy storage system with net metering, as the best-optimised option with energy generation cost of INR 4.21/kWh, with 6.15 years payback period. The study results can be followed for sustainable solar power generation for commercial grid connected PV power plants worldwide.

Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm

Electric vehicles (EVs) are quickly becoming a staple of smart transportation in applications involving smart cities due to their ability to reduce carbon footprints. However, the widespread use of electric vehicles significantly strains the nation's electrical system. In-depth descriptions of the EV's energy management system (EMS) should highlight the vehicle's powertrain's vital role. The energy for propulsion in electric automobiles comes from a rechargeable battery. The safe and dependable operation of batteries in electric vehicles relies heavily on online surveillance and status estimations of charges. An energy management strategy (EMS) that considers the electric vehicle's battery and ultra-capacitor may lessen the vehicle's reliance on external power sources and extend the battery's lifespan. A machine learning-based mathematical dynamic programming algorithm is used in designing the energy management system to teach the system how to respond appropriately to various situations without resorting to predefined rules. Therefore, this research aims to use Machine Learning to create a Smart Energy Management System for Hybrid Electrical Vehicles (SEMS-HEV) with energy storage. Energy optimization techniques and algorithms are necessary in this setting to reduce expenses and length of charging and appropriately arrange the EV charging process to prevent bursts in the electrical supply that may impact the transmission network. To improve the performance of an energy management system, this study employs an IoT-based smart charging system for scheduling V2G connections for hybrid electrical vehicles. It allows for more precise and effective control and greater efficiency by enabling the system to learn from its surroundings.

An IoT-based intelligent smart energy monitoring system for solar PV power generation

Abstract As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.

Experimental investigation of a novel smart energy management system for performance enhancement of conventional solar photovoltaic microgrids

Solar photovoltaic microgrids are reliable and efficient systems without the need for energy storage. However, during power outages, the generated solar power cannot be used by consumers, which is one of the major limitations of conventional solar microgrids. This results in power disruption, developing hotspots in PV modules, and significant loss of generated power, thus affecting the efficiency of the system. These issues can be resolved by implementing a smart energy management system for such microgrids. In this study, a smart energy management system is proposed for conventional microgrids, which consists of two stages. First power production forecasting is done using an artificial neural network technique and then using a smart load demand management controller system which uses Grey Wolf optimiser to optimize the load consumption. To demonstrate the proposed system, an experimental microgrid setup is established to simulate and evaluate its performance under real outdoor conditions. The results show a promising system performance by reducing the conventional solar microgrids losses by 100% during clear sunny conditions and 42.6% under cloudy conditions. The study results are of relevance to further develop a smart energy management system for conventional microgrid Industry and to achieve the targets of sustainable development goals.

Analysis of AWPI Based Hybrid Grid-tied Inverter for Smart Energy Management System

Renewable energy sources are gaining popularity in recent years for power generation. The world has an abundance of pollution-free solar and wind energy; batteries play vital role for energy storage and all these sources combine to form a hybrid power system. Efficient management of the hybrid system via controlling the operation of its components is essential. In this article, a smart energy management system is proposed, where the control is focused on the hybrid grid-tied inverter. The inverter module is controlled through an anti-windup proportional-integral (AWPI) current and voltage controller. The performance of the proposed control system has been analyzed through current, and voltage Total Harmonic Distortion (THD) before and after grid integration. The suggested AWPI controller effectively mitigated the voltage drop and maintained the THD within the appropriate level. In addition, the proposed scheme of interest enhanced the steady-state response of the smart-grid system. The comparative analysis made among the intended controller and its counterparts inferred its superiority.

BSEMS-A Blockchain-Based Smart Energy Measurement System.

The modern world's increasing reliance on automated systems for everyday tasks has resulted in a corresponding rise in power consumption. The demand is further augmented by increased sales of electric vehicles, smart cities, smart transportation, etc. This growing dependence underscores the critical necessity for a robust smart energy measurement and management system to ensure a continuous and efficient power supply. However, implementing such a system presents a set of challenges, particularly concerning the transparency, security, and trustworthiness of data storage and retrieval. Blockchain technology offers an innovative solution in the form of a distributed ledger, which guarantees secure and transparent transaction storage and retrieval. This research introduces a blockchain-based system, utilising Hyperledger Fabric and smart contracts, designed for the secure storage and retrieval of consumers' energy consumption data. Finally, a user-friendly web portal was designed and developed using the node.js framework, offering an accessible and intuitive interface to monitor and manage energy consumption effectively.

Assessment of Energy and Heat Consumption Trends and Forecasting in the Small Consumer Sector in Poland Based on Historical Data

The paper outlines the methodology employed for desk-based research, which involved gathering and analyzing empirical data on energy and heating consumption in the Polish small consumer sector. Secondary sources, including reports, documents, scientific publications, and public statistics, were utilized to ensure a comprehensive understanding of the subject matter. The research methodology ensured the acquisition and examination of reliable and extensive data regarding energy and heat consumption among small consumers in Poland. The study investigated consumption trends of hard coal, electricity, and natural gas in Polish households from 2006 to 2021. The findings revealed an increasing pattern in electricity and natural gas consumption, alongside a simultaneous decline in the usage of hard coal. Future consumption was estimated using trend models, and factors contributing to changes in energy consumption patterns were examined, with forecasts to 2027. To achieve decarbonization and address climate objectives, the study underscores the need to increase the proportion of renewable energy sources and boost energy efficiency. The significance of reducing household energy consumption through enhanced insulation, smart energy management systems, and low-carbon alternatives is emphasized. Additionally, the study addresses Poland’s future energy plans as a proactive step toward decarbonizing the national economy. In summary, the study furnishes valuable insights into energy consumption trends and their determinants in the Polish small consumer sector.

SREM: Smart renewable energy management scheme with distributed learning and EV network

In this article, aiming to develop the Green Internet of Vehicles (G‐IoV), we propose a smart energy management system that leverages the intelligence edge clients and the distributed electric vehicles (EVs). The system proposed in this article incorporates the benefits of both software, specifically in terms of the user interface, and hardware, specifically in terms of edge clients. In particular, this system integrates intelligence edge clients with an EV CAN bus network as an electronic control unit. By leveraging the intelligent edge clients recommendation system, EVs can make informed decisions on battery charging or discharging actions. As a result, a virtual‐power‐plant (VPP) can treat the EVs network as a vast intelligent energy storage facility, efficiently managing the battery energy of all distributed EVs connected to the platform and fully utilizing the electricity generated from renewable energy sources. We experimentally verify that using federal learning to train models in EV networks versus training models directly in EVs, using federal learning in EV networks yields better experimental results.

Towards Nearly Zero-Energy Buildings: Smart Energy Management of Vehicle-to-Building (V2B) Strategy and Renewable Energy Sources

As countries worldwide strive for net-zero emissions, electric vehicles (EVs) have gained significant momentum. EVs, particularly through Vehicle-to-Everything (V2X) technology, including Vehicle-to-Building (V2B), are recognized as key enablers for achieving nearly-zero energy buildings (NZEB). This research presents an advanced Smart Energy Management System (SEMS) with a novel V2B strategy, offering precise one-hour resolution to reduce grid reliance and optimize peak load management. The SEMS coordinates building energy and EV charging while harnessing the potential of renewable energy resources and battery storage systems. By carefully scheduling EV charging and discharging activities, it maximizes the efficient use of available energy resources. Importantly, the algorithm integrates real-world EV parking patterns, accounting for factors such as fleet composition, parking durations, and charging priorities. Economic evaluation encompasses not only direct EV battery degradation costs but also external expenses associated with environmental impacts. Simulation outcomes underscore a remarkable reduction in building grid power demand (65%) and a substantial decrease in carbon emissions (64%). The PV+V2B Scenario emerges as the preferred choice, striking an optimal balance between initial investment costs, energy savings, and carbon footprint reductions. This study holds great academic and practical significance, contributing to the advancement of smart, sustainable, and economically viable net-zero cities.

Experimental analysis of a commercial size bio-based latent thermal energy storage for air conditioning

Future smart and efficient energy management systems for space cooling and heating in building applications require novel solutions to store heat to decouple the energy demand and the availability of renewable energy sources. Latent thermal energy storages represent one of the most promising solutions; however, their cost-effective implementation in terms of energy and cost savings and payback time needs to be verified case by case. Thus, simplified, though accurate, performance correlations during both charging and discharging phases are strongly needed to be implemented into the different dynamic simulation tools. This work is intended to present the experimental behavior of a novel 18 kWh latent thermal energy storage that uses the roll-bond technology to efficiently store and release cold energy by exploiting the solid/liquid phase change process of 300 kg of a biobased phase change material having a melting temperature of 9 °C. The effects of the inlet water temperature and flow rate are studied to elucidate how the performance varies and to identify possible control strategies. On the basis of the experimental results, two correlations are proposed to accurately predict the performance of the latent thermal energy storage to be implemented in general dynamic simulation tools that can help in evaluating the system performance and the achievable energy and cost savings.

Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles

As the process of urbanization continues to accelerate, the demand for sustainable cities has become more critical than ever before. The incorporation of electric vehicles (EVs) is a key component in creating sustainable cities. However, the development of smart cities for EVs entails more than just the installation of charging stations. Software engineering plays a crucial role in realizing smart cities for electric vehicles. This paper examines the role of software engineering in the creation of smart cities for electric vehicles, the techniques utilized in electric vehicle charging infrastructure, the obstacles faced by software engineers, and the future of software engineering in sustainable cities. Specifically, the paper explores the significance of software engineering in integrating EVs into the transportation system, including the design of smart charging and energy management systems, and the establishment of intelligent transportation systems. Additionally, the paper offers case studies to demonstrate successful software engineering implementations for smart cities. Finally, the paper concludes with a discussion of the challenges that software engineers encounter in implementing intelligent transportation systems for EVs and provides future directions for software engineering in sustainable cities.

Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC.

Smart Energy Management for a Microgrid System Based on Echo State Networks─Gray Wolf Optimizer Hybrid Algorithm

This work present a smart energy management system for a microgrid based on a hybrid intelligent adaptive algorithm composed of an echo state network and gray wolf optimizer techniques. The microgrid connected to the grid is composed of renewable sources such as photovoltaic and wind conversion associated with a hybrid battery hydrogen fuel generation storage system. An adaptive intelligent control unit is developed to manage the power flow between the hybrid power sources and other devices in the system. The controller chooses the optimal operating mode of power sources ensuring the continuous supply of the injecting power in the grid and maintaining the battery state of charge at acceptable levels as the hydrogen tank level. Complete mathematical modeling for the proposed system is implemented in MATLAB/Simulink to track the microgrid performance. Numerical and graphical results are presented to show the effectiveness of the proposed control system

Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.