Smart Home Energy Management Research Articles

Day-ahead multi-criteria energy management of a smart home under different electrical rationing scenarios

With the recent global energy crisis, some countries have implemented electrical rationing (ER), making it necessary for smart homes to play a pivotal role in optimizing energy consumption and contributing to sustainable practices. To effectively manage smart home consumption, a stochastic programming approach for a grid-connected smart home energy management system (SHEMS) is proposed in this paper. The system includes PV, battery, diesel, and gas-based heating/cooling systems (HCS). Additionally, a demand response program (DRP) has been employed under time-of-use tariffs in the Syrian energy market. The main objective is to minimize the day-ahead expected cost and consumer discomfort by optimizing the operation of dispatchable units and loads. To manage the risks associated with the expected cost due to potential uncertainties in PV energy generation and electrical rationing programs, the conditional value-at-risk (CVaR) approach is adopted. Two methods are proposed to model the uncertainty in PV energy generation; interval bands and interval-based scenarios. The problem is modeled as a mixed-integer non-linear programming (MINLP) model, and coded in GAMS to test different cases. Based on the results obtained, substantial reductions reached 56.2% in worst-case cost scenarios when employing concurrent DRP-risk management.

Advances in fuzzy control systems for energy management in smart homes

The drive towards energy efficiency and sustainability has spurred technological innovations for managing energy consumption in residences, with fuzzy control systems standing out. These systems, based on fuzzy logic, offer adaptive and intuitive solutions to optimize energy use in smart homes, dealing with uncertainties and imprecisions in environmental conditions and occupant preferences. Fuzzy logic allows for refined control of devices such as lighting, heating, ventilation, and air conditioning (HVAC), adjusting in real-time to variations in conditions and user behaviors. Recent studies have demonstrated the effectiveness of these systems in various applications. Kontogiannis, Bargiotas, and Daskalopulu (2021) developed a fuzzy control system to recommend optimal energy consumption values based on environmental data, using the Mamdani approach and decision tree linearization. Keshtkar and Arzanpour (2017) introduced an autonomous thermostat combining supervised learning with wireless sensors and dynamic electricity pricing, adjusting temperatures and maintaining user comfort. Ain et al. (2018) proposed a Fuzzy Inference System that incorporates humidity and internal temperature variations to optimize energy consumption without sacrificing comfort. Additionally, Khalid et al. (2019) presented an energy management controller for smart grids using fuzzy logic and heuristic optimization techniques, improving load management and reducing costs and consumption. Collotta and Pau (2015) explored the integration of IoT technology with fuzzy-based systems using Bluetooth Low Energy (BLE) to manage energy consumption in home automation networks. These studies highlight that fuzzy control systems are crucial for optimizing energy consumption in smart homes, balancing efficiency and comfort, with ongoing advancements promising continuous improvements in energy management.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation.

Realistic appliance power consumption data are essential for developing smart home energy management systems and the foundational algorithms that analyze such data. However, publicly available datasets are scarce and time-consuming to collect. To address this, we propose HYDROSAFE, a hybrid deterministic-probabilistic model designed to generate synthetic appliance power consumption profiles. HYDROSAFE employs the Median Difference Test (MDT) for profile characterization and the Density and Dynamic Time Warping based Spatial Clustering for appliance operation modes (DDTWSC) algorithm to cluster appliance usage according to the corresponding Appliance Operation Modes (AOMs). By integrating stochastic methods, such as white noise, switch-on surge, ripples, and edge position components, the model adds variability and realism to the generated profiles. Evaluation using a normalized DTW-distance matrix shows that HYDROSAFE achieves high fidelity, with an average DTW distance of ten samples at a 1Hz sampling frequency, demonstrating its effectiveness in producing synthetic datasets that closely mimic real-world data.

LSTM Networks for Home Energy Efficiency

This study aims to develop and evaluate an LSTM neural network for predicting household energy consumption. To conduct the experiment, a testbed was created consisting of five common appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart meters within a Home Energy Management System (HEMS). Additionally, a meter was installed on the distribution board to measure total consumption. Real-time data were collected at 15-min intervals for 30 days in a residence that represented urban energy consumption in Sincelejo, Sucre, inhabited by four people. This setup enabled the capture of detailed and specific energy consumption data, facilitating data analysis and validating the system before large-scale implementation. Using the detailed power consumption information of these devices, an LSTM model was trained to identify temporal connections in power usage. Proper data preparation, including normalisation and feature selection, was essential for the success of the model. The results showed that the LSTM model was effective in predicting energy consumption, achieving a mean squared error (MSE) of 0.0169. This study emphasises the importance of continued research on preferred predictive models and identifies areas for future research, such as the integration of additional contextual data and the development of practical applications for residential energy management. Additionally, it demonstrates the potential of LSTM models in smart-home energy management and serves as a solid foundation for future research in this field.

Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid

This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.

Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic–thermal systems

The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic Algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.

Reinforcement learning layout‐based optimal energy management in smart home: AI‐based approach

AbstractThis research addresses the pressing need for enhanced energy management in smart homes, motivated by the inefficiencies of current methods in balancing power usage optimization with user comfort. By integrating reinforcement learning and a unique column‐and‐constraint generation strategy, the study aims to fill this gap and offer a comprehensive solution. Furthermore, the increasing adoption of renewable energy sources like solar panels underscores the importance of developing advanced energy management techniques, driving the exploration of innovative approaches such as the one proposed herein. The constraint coordination game (CCG) method is designed to efficiently manage the power usage of each appliance, including the charging and discharging of the energy storage system. Additionally, a deep learning model, specifically a deep neural network, is employed to forecast indoor temperatures, which significantly influence the energy demands of the air conditioning system. The synergistic combination of the CCG method with deep learning‐based indoor temperature forecasting promises significant reductions in homeowner energy expenses while maintaining optimal appliance performance and user satisfaction. Testing conducted in simulated environments demonstrates promising results, showcasing a 12% reduction in energy costs compared to conventional energy management strategies.

Wasserstein generative adversarial networks-based photovoltaic uncertainty in a smart home energy management system including battery storage devices

Rooftop photovoltaic (PV) power generation uncertainty is one of the prominent challenges in smart homes. Home Energy Management (HEM) systems are essential for appliance and Energy Storage System (ESS) scheduling in these homes, enabling efficient usage of the installed PV panels' power. In this context, effective solar power scenario generation is crucial for HEM load and ESS scheduling with the objective of electricity bill cost reduction. This paper proposes a two-step approach, where a machine learning technique, Wasserstein Generative Adversarial Networks (WGANs), is used for PV scenario generation. Then, the generated scenarios are used as input for the HEM system scheduler to achieve the goal of cost minimization. The generated solar energy scenarios are considered in a single household case study to test the presented method's effectiveness. The WGAN scenarios are evaluated using different metrics and are compared with the scenarios generated by Monte Carlo simulation. The results prove that WGANs generate realistic solar scenarios, which are then used as input to a Mixed Integer Linear Programming (MILP) problem aiming for electricity bill minimization. A 41.5% bill reduction is achieved in the presented case study after scheduling both the load and ESS, with PV fluctuations taken into account, compared to the case where no scheduling, PV, or ESS are considered.

Intelligent-of-things multiagent system for smart home energy monitoring

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decision-making efficiency.

Smart home energy management and active power loss analysis of a residential community

As the demand for energy continues its upward trajectory, judiciously managing the global energy resources has become increasingly imperative. One effective strategy to accomplish this efficiency is by strategically scheduling intelligent household appliances, a process that considers dynamic pricing schemes and the availability of renewable energy sources. This paper presents a comprehensive study that delves into optimizing smart home appliance utilization, considering electric vehicles and a modest rooftop photovoltaic installation. The study seeks to ascertain their potential impact on both energy consumption and cost mitigation. The proposed scheduling approach is centered on the optimal scheduling of smart appliances, harnessing the power of real-time pricing schemes to curtail the overall energy expenditure. The scheduling conundrum is meticulously modelled using General Algebraic Modeling System software and subsequently tackled by the CPLEX solver, leveraging mixed-integer linear programming techniques. Results reveal a remarkable achievement, with the potential for up to an 80.6 % reduction in the overall cost compared to the base case under various scenarios considering different approaches. To validate the integrity of the study, outcomes are juxtaposed with those of the base paper, and the superiority of the proposed method is evident from the results. Furthermore, the paper considers the ramifications of optimized scheduling on the voltage profile and overall system losses nearly 7 % less compared to the base case within the context of the IEEE 33-bus system. Thus, the presented work successfully analyzes the effect of household energy scheduling on the community level and scales up to a microgrid level.

AI-driven innovations in automation and urban management

This paper examines the transformative impact of Artificial Intelligence (AI) across diverse sectors, particularly focusing on industrial automation, smart homes, and intelligent cities. It delves into how AI-driven technologies enhance predictive maintenance, quality control, and supply chain optimization in industrial settings, contribute to energy management, security enhancement, and health monitoring in smart homes, and improve traffic management, waste management, and public safety in intelligent cities. Through detailed analyses and quantitative assessments, the study showcases the efficiency gains, cost reductions, and quality of life improvements facilitated by AI integration. It highlights the pivotal role of AI in addressing contemporary challenges and setting new standards for operational excellence, sustainability, and safety.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources

In this article, smart home energy management is proposed using real-time pricing (RTP) based demand response for effective utilization of renewable-based distributed generation (DGs), battery, and electric vehicle (EV) to reduce grid dependency. The smart home consists of eighteen smart appliances and EV. The EV can be operated either in vehicle-to-home or home-to-vehicle. The smart appliances in the smart home are categorized into two types: electrical and thermal loads. Each smart appliance’s operating time slots and durations are scheduled based on the tariff, availability of DGs, and storage devices using the Binary Particle Swarm Optimization (BPSO) algorithm without affecting user preferences. Due to the proposed method, the grid dependency of the smart home in each time slot is reduced, which shall reduce the net electricity cost. Different cases are analyzed based on the various combinations of renewable-based DGs such as wind power, solar photovoltaic (PV), and battery in the smart home to showcase the effectiveness of the proposed method. The performance of the proposed system is analyzed under uncertain EV and DG operations. A detailed comparison is made with and without appliance scheduling in the proposed smart home to showcase the necessity of appliance scheduling and reduce the grid dependency. The simulation results show the profit of ₹13.046 and the grid dependency reduced by 9.636 kW in case 5 with appliance scheduling compared to case 5 without appliance scheduling. Therefore, the electricity cost and grid dependency are reduced using the BPSO algorithm for appliance scheduling in the smart home under normal and uncertain conditions.

An integrated system of energy generation, storages, and appliances consumption based on machine learning techniques and internet of things

In the swiftly evolving micro-grid landscape, this paper explores the pivotal role of residential consumers and their active engagement. The primary focus is on revolutionizing customer management through the integration of smart homes, with an emphasis on facilitating demand response and bolstering operational efficiency. Also, the innovative Power Generation, Electricity Storages, and Energy Consumption in Smart Homes (PG–ES–ECSH) approach is introduced, meticulously designed for next-generation technologies, and delve into an smart home load control system. This system, employing Extreme Machine Learning (EML) and an imprecise reasoning framework, aims to enhance computational efficiency in managing diverse electrical energy sources and small-scale power generation appliances. The decision-making process of the framework incorporates real-time monitoring of environmental data, power generation, and battery storage. Our research significantly contributes to the evolution of smart homes and micro-grids by embracing technologies crucial for the future, such as machine learning and the Internet of Things (IoT). In this paper, the application of IoT and EML are used to enhance the connection between users in the microgrid and the learning rate of the proposed system, respectively. Emphasizing the burgeoning significance of energy management in smart homes, our study underscores the profound impact of energy resources on overall quality of life. With the increasing prevalence of smart homes, there is a growing demand for efficient energy utilization and management. Noteworthy is the PG–ES–ECSH method's impressive accuracy ratio of 96 % and a precision rate of 92 %. These findings highlight the imperative need for cost-effective energy management solutions, particularly in the face of escalating electricity consumption and the integration of novel power resources.

Design and implementation of smart home energy management system for Indian residential sector

Residential power consumption has surged with the widespread adoption of smart appliances, prompts utilities to implement Demand Response(DR) programs. Home Energy Management Systems (HEMS) plays a vital role in these initiatives within the smart grid infrastructure. The rapid advancement of wireless communication technologies, especially the Internet of Things (IoT), will enable successful architectural designs for HEMS. This study proposes an incentive-based DR implementation using IoT-enabled HEMS for the Indian residential sector that represents 25% of the total power demand. The proposed HEMS automates the management of categorized loads during peak hours through load shifting and scheduling. The proposed algorithm is deployed in real-time residential setup using IoT modules. Emphasizing sustainability and consumer affordability, the work integrates a Photo-Voltaic (PV) based Battery Storage System (BSS) to support critical loads during peak hours. Consumer load preferences obtained through a survey are incorporated into the deployment of the proposed HEMS. Experimental studies demonstrated significant energy savings of 49% with the loads under consideration and substantial cost savings like approximately 49% with Load Management (LM) alone, 53% with the inclusion of Time of Day (ToD) tariff, and an impressive 78% with the proposed incentives for participating in DR. Additionally, this study highlights the synergistic savings realized through the combined implementation of the proposed LM strategy and ToD tariff in India.

The Importance of AI-Enabled Internet of everything Services for Smart Home Management

Abstract Smart home applications are ubiquitous and have become popular because of the overwhelming use of the Internet of Things (IoT) and artificial intelligence (AI). Living smart with automation and integrated AI-IoT has become more affordable as home automation technologies have matured. In addition, the Internet of Everything (IoE), which involves the interconnection of humans, businesses, and intelligent objects, has the potential to reshape various industries. However, the rising energy cost and demand have led numerous organizations to determine smart ways to monitor, control, and save energy. Hence, this study suggests AI-Enabled Internet of Everything Services (AI-IoES) for efficient smart home energy management. The data have been taken from the Open Smart Home IoT//Energy Dataset for analyzing the energy consumption of home appliances. This paper presents an IoT sensor for energy management to track and control specific loads in smart homes. The deep neural network (DNN) is built for secure demand-side management (DSM) in an IoT-assisted smart grid and trained on the extracted feature from electricity consumption information gathered using an IoT sensor. The system is established with real-time monitoring and a user interface for remote control and access. The experimental outcome demonstrates that the suggested AI-IoES system increases the user experience by 98.9%, energy efficiency ratio (EER) by 97.8%, and accuracy ratio by 97.2%, and reduces energy consumption by 19.2% compared with other existing methods.

An enhanced coati optimization algorithm for optimizing energy management in smart grids for home appliances

This research presents an innovative approach to energy management in smart homes, aiming to efficiently regulate energy demands while ensuring customer loyalty. The focus is on addressing the limitations of existing demand-side management (DSM) programs, which predominantly target the residential sector. The proposed solution introduces the Adaptive Coati Optimization algorithm, which optimizes device organization based on Critical-Peak-Price and Real-Time-Price power payment systems. By strategically managing energy consumption, the algorithm reduces electrical expenses and peaks without compromising user convenience. The study evaluates the effectiveness of the approach across three operational periods (60 minutes, 12 minutes, and 24 minutes) to align with varying customer needs. Overall, the Adaptive Coati Optimization algorithm offers a promising solution for cost-efficient energy management in smart homes, combining both financial benefits and enhanced user satisfaction. The results indicate a significant decrease in tariffs rates, up to 30%, leading to a 20% increase in user satisfaction and a 25% improvement in cost utilization.

Adaptive cloudiness index for enhanced photovoltaic energy prediction and management in low-income smart homes using geographic information system

Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of Photovoltaic (PV) uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System (GIS) models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case in an African developing location, i.e. Cairo. Egypt.

Smart Home Energy Management: VAE-GAN Synthetic Dataset Generator and Q-Learning

Smart Home Energy Management: VAE-GAN Synthetic Dataset Generator and Q-Learning