Smart Home Energy Management System Research Articles

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.

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.

A price-regulated scheduling of electric vehicle with grid supporting photovoltaic and battery storage

With solar photovoltaic (PV) sources integrated into microgrids and electric vehicle (EV) charging stations, users have a variety of charging mode options to meet their needs. However, overloading of the distribution transformer, higher peak demand, and voltage instability are all potential drawbacks associated with EV charging loads. Microgrid optimisation becomes more difficult when dealing with intermittent PV generation and uncertain EV charging and discharging profiles. A Smart Home Energy Management System (SHEMS) is proposed for efficient power flow from PV sources, battery energy storage system (BESS), and an EV to maximise economic advantages for a consumer using the Artificial Hummingbird Algorithm (AHBA)-based optimisation approach. This approach considers the EV's unpredictable energy consumption, variations in residential load demand and seasonal variations in the solar insolation profile throughout the year. Consequentially, potential EV requirements such as initial and predetermined state of charge (SOC) arrangements and arrival and departure hours are also considered. Three energy management scenarios satisfying the allowable loading limit of the distribution transformer are examined, namely power transfers from home to electric vehicle (H2EV), EV to home (EV2H), and EV to grid (EV2G). The reduction in yearly operating costs (YOC) of 49.5 %, 53.2 %, and 70 % was achieved in H2EV, EV2H, and EV2G modes, respectively. This demonstrates that an effective EV and BESS management strategy can reduce the annual operating costs of a residential consumer by up to 70 % while simultaneously achieving peak load shaving.

Optimal resilient operation and sustainable power management within an autonomous residential microgrid using African vultures optimization algorithm

This paper introduces an adaptive artificial intelligence (AI)-based home energy management system (HEMS) to mitigate the discomfort of customers and reduce operational costs. A techno-economic smart HEMS is proposed for home microgrid management, employing two control approaches. The initial approach involves employing an FPGA as a central control unit due to its high-speed processing capabilities and efficient handling of rapid changes within desired limits. The other approach aims to optimize the performance of the microgrid and obtain an optimal operating plan for home microgrids by integrating power limitations using a developed coordinated strategy. A multi-objective optimization problem is formulated that involves the coordinated operation of backup sources. The study utilizes the African Vultures Optimization Algorithm (AVOA) and compares it with newly introduced algorithms, with a specific emphasis on three techno-economic objectives. The simulation and experimental results indicate that the AI-embedded FPGA-based HEMS not only enhances performance and extends Battery Energy Storage Systems (BESSs) life but also reduces peak times resulting from random electric vehicle charging. Through comparative analysis, the superiority of the AVOA is evident, attaining the lowest operating cost of 1308.85$ by the end of the day, reflecting a 3.77% reduction in operating costs compared to the previous study.

ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately 16 20 times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.

General Overview and Proof of Concept of a Smart Home Energy Management System Architecture

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality.

Retracted: Development and Application of Smart Home Energy Management System Based on Wireless Network Technology

Retracted: Development and Application of Smart Home Energy Management System Based on Wireless Network Technology

Integrating Vehicle-to-Home Unit with Smart Home Energy Management Systems: A Unified Energy Management Framework

The increasing acceptance of electric vehicles (EVs) and renewable energy sources (RES) has led to the need for a unified energy management framework that can integrate multiple energy systems, including Vehicle-to-Home (V2H) units, Smart Home Energy Management Systems (SHEMS), Solar PV systems, and the grid. The framework uses a centralized control strategy that makes decisions based on real-time integration of a Vehicle-to-Home (V2H) unit with a Smart Home Energy Management System (SHEMS), which can provide a solution to these challenges by enabling bidirectional power flow between the EV battery and the home. We proposed a unified energy management framework that integrates a Ve- hicle-to-Home (V2H) unit with a SHEMS using a Perturb and Observe (P-&-O) algorithm. The proposed framework allows for the optimization of energy usage in the home by utilizing the battery of an EV, which can be charged during off-peak hours and discharged during peak hours. The P&O algorithm is a well-known algorithm used in photovoltaic systems to track the Maximum Power Point (MPP) of a solar panel. The algorithm is also applicable to EV charging and discharging systems. The algorithm uses a per- turbation signal to change the charging rate of the EV battery and observes the resulting change in the bat- tery voltage. The simulation results can also provide benefits to EV owners, such as reduced electricity costs and increased flexibility in managing their EV charging, helping to achieve efficient and sustainable energy management in residential areas. Keywords: Solar panel, Electric Vehicle Battery, Vehicle-to-Home (V2H), Smart Home Energy Manage- ment Systems (SHEMS), Demand side management.

Intelligent energy management system for smart home with grid-connected hybrid photovoltaic/ gravity energy storage system

A dynamic smart home energy management system (SHEMS) is proposed in this study to address the growing concerns of energy conservation and environmental preservation. This study contributes a novel one-week dynamic forecasting model for a hybrid PV/GES system integrated into a smart house energy management system, encompassing dynamic electricity pricing, smart appliance control, PV generation forecasting, and gravity energy storage state of charge prediction. The findings of this study demonstrate that the developed dynamic SHEMS model significantly reduces household energy use and lowers the cost of power. With this SHEMS model, the hybrid PV/GES can supply the house's energy needs for eight and a half hours each day. In addition, it offers the advantage of low electricity price for charging the battery of the electric vehicle. Performance indicators such as RMSE and MAPE are employed, yielding forecast error results ranging from 13.45 % to 23.16 % for RMSE and 4.06 % to 11.27 % for MAPE.

A PROTOTYPE DESIGN OF A DEDICATED SMART HOME ENERGY MANAGEMENT SYSTEM

<em>With the continuous rise of electrical energy costs for heating, cooling, and ventilation and the increasing number of energy-consuming home appliances, designing a cost-effective efficient residential electric energy management system has become crucial. This paper presents a prototype simulated design of a Smart Home Energy Management System (SHEMS), dedicated to the management of residential electrical energy according to the user-preassigned comfort requirements and cost criteria. All planted sensors and home appliances are controlled by one master microcontroller. The Proteus Software ISIS was used for all simulation results. The proposed application collects information from sensors to control the required home appliances by switching them ON or OFF based on a specified design algorithm. The sensors' assigned values and home appliance status are stored in a main database. The proposed design also allows home appliances to be monitored and controlled remotely via the Internet. Simulated results are given for a two-room residential home</em>.

A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data

The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading.

Design and Implementation of Smart Energy Management System

It is crucial to create systems that lessen our reliance on non-renewable resources in a world with declining energy reserves and escalating economic crises. Smart Home Energy Management Systems, which employ smart switches to actively monitor energy use by each load, available power, and other parameters to transition between renewable resources and the primary power grid, are becoming more and more popular. The results of several methodologies were combined and analysed after a thorough literature review. The approach described is a synthesis of all the most well-liked approaches. The suggested methodology aims to implement demand response management simultaneously on grid electricity and solar power. The issue was divided into two scheduling scenarios: one assuming only solar power is available, the other assuming just grid power. In order to use the resources available as effectively as possible, the two halves are then combined.

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques.

Lens-oppositional duck pack algorithm based smart home energy management system for demand response in smart grids

Smart grid (SG) is one of the emergent technologies providing effective solutions for increased power demand globally. Due to the advances of Internet of Things (IoT), smart devices are integrated with the smart home (SH) environment which actively participates in electricity market through demand response (DR) programs for energy management. DR refers to the modifications in the electricity utilization of the end user from the common utilization pattern in response to the variation in electricity prices. An effective energy management system (EMS) with price-based DR model is essential for IoT enabled SH environment. In this view, this article develops a Lens-Oppositional Duck Pack Algorithm based Smart Home Energy Management System (LODPA-SMEMS) for DR in SGs. The major focus of the LODPA-SMEMS model is to achieve optimal management of energy usage by scheduling process. The LODPA is derived by the incorporation of lens-oppositional based learning (LOBL) with conventional DPA. The LODPA-SMEMS model is mainly derived to reduce peak-to-average ratio (PAR) and electricity price with maximum user comfort (UC). The experimental outcome of the LODPA-SMEMS model outperforms the other methods under several aspects. The results reported that the LODPA-SMEMS model improves overall energy usage and consequently, the sustainability of IoT enabled SH gets enhanced. According to the findings, the LODPA-SMEMS model increases overall energy efficiency, hence increasing the sustainability of IoT-enabled SH. The experimental values demonstrated that the LODPA-SMEMS model has accomplished lower PAPR of 2.10 whereas GA, BPSO, GWDO, GBPSO, and WBFA models have attained higher PAPR of 3.70, 3.10, 3.80, 3.60, and 2.40 respectively. Also, EEC analysis of LODPA-SMEMS on DAPS, RTPS, TUPS: 3.53kWh, 1.81kWh, 1.85kWh. Similarly, LODPA-SMEMS model has achieved superior performance with least ET of 68 s.

“It's never telling me that I'm good!” Household experiences of testing a smart home energy management system with a personal threshold on energy use in Sweden

Although smart technologies are widely promoted as enabling flexibility in households' energy demand, they often fail to achieve substantial impact. How smart technologies are actually used and to what extent they enable changes of energy-reliant practices in everyday life therefore needs to be better understood. This paper evaluates a smart home energy management system, Ero 2.0, which was tested by households in a multi-residential building in Sweden. To our knowledge, apart from its forerunner, Ero 2.0 is the first of its kind to include a personal threshold on energy use varying with the availability of preferred energy sources. Semi-structured interviews were conducted with 15 participants, complemented by pre and post surveys, answered by 39 and 32 participants respectively. Overall, Ero 2.0 contributed to raised awareness regarding electricity and water use. To some extent, it also contributed to changes in practices, mainly through changed or reduced use of certain functions. However, it was less effective in enabling shifting practices in time. Difficulties to change practices were found to depend on: (1) lack of flexibility in everyday life, (2) limitations in size and layout of the dwelling, (3) lack of incentives and perceived impact, (4) lack of guidance and (5) lack of possibilities to control devices through the interface. The second point is an aspect that to date has received little attention in smart energy research. Design opportunities for future smart home energy management systems are discussed while acknowledging that such technologies cannot alone achieve the transition to more sustainable energy systems.

A Transfer Reinforcement Learning Framework for Smart Home Energy Management Systems

Transfer learning (TL) is widely used as a solution to overcome the huge amount of time required to train a deep learning model. However, a number of challenges are associated with transferring the knowledge between the same and different target domains (TDs), for instance, designing efficient mapping functions, correlating the source and target datasets, identifying necessary parameters for enabling TL, and so on. In this article, we, therefore, present a solution to enable TL for home energy management systems (HEMSs) in smart homes consisting of multiple residents and appliances. This led to eliminating the training time and data requirements associated with training home appliances. A mapping function is designed based on correlating the source and target datasets before transferring the knowledge. Furthermore, a training model is designed to iteratively train a source model, i.e., expert home (EH) and expert appliance (EA), with source dataset and feedback knowledge from the target model, i.e., learner home (LH) and leaner appliance (LA). An extensive set of simulations is carried out to evaluate the performance of the proposed scheme. Simulation results show that transferring knowledge in both the same and different domains significantly reduces the energy consumption of individual home appliances and smart homes. In the future, we will expand the research using new models to promote TL effectiveness.

Design of type-2 fuzzy logic controller in a smart home energy management system with a combination of renewable energy and an electric vehicle

Home energy management is one of the most important parts of a smart home that manages the efficient use of energy in the smart home. This paper aims to design two type-2 fuzzy logic controllers in the demand-side energy management system. For this purpose, a combination of renewable energy sources, such as fuel cells, photovoltaic solar panels, vertical axis wind turbines with a helical savonius rotor, and an electric vehicle and energy storage system along with an external grid are utilized to supply the electricity usage of home appliances in a smart home. By using the proposed method, higher quality, more economical, and environmentally friendly energy can be accomplished. To this end, the outputs of the proposed controllers are defined to make appropriate decisions about the generated energy, how to supply energy for own consumption, and controllable loads. The number and distribution of membership functions of type-2 fuzzy logic controllers are selected according to the measured real input data over a year in Tehran, Iran. To demonstrate the effectiveness of the proposed method it is simulated by MATLAB/Simulink software, which indicates that the proposed system causes the smart home to receive 49.186 kWh less electricity energy from the grid to supply daily power for home appliances and therefore can weekly consume approximately 343.95 kWh less electricity energy from the grid. it is shown that after applying the proposed strategy, electricity costs were reduced by 71.5%, and the peak-to-average ratio was reduced by 64.6%.

Development of a Multi-Asset Risk Assessment Algorithm in the Context of Home Energy Management

Risk management has become an important concern in the light of current developments in the home energy management sector as well as within the broader considerations regarding the building sector’s energy production and consumption paradigm. The current multi-parameter energy ecosystem structure raises a number of new challenges that require a reliable and robust risk management framework to assist in building management decision making. This paper presents a multi asset risk assessment algorithm, which is part of a risk management application developed for residential buildings within the framework of energy communities and digital energy markets. It describes the logic, principles, and operation of the algorithm, as well as the functionalities related to risk analysis and result visualization. This underpins the necessary means to monitor elements of a home energy system as well as tools for risk prevention and mitigation. The proposed application provides accurate, detailed, and easy to use information to assist decision makers and stakeholders in the context of smart home energy management systems.

Artificial Intelligence Based Smart Home Energy Management System

Recently, countries are faced with an unreliable electric power supply to homes and the industry due to insufficient supply from the utility, causing load shedding. With the power utilities struggling to keep the lights on, there are initiatives to invest in backup power solutions and home energy management systems (HEMS) to manage this development. This paper analyzes energy consumption with the existing infrastructure of typical equipment used in homes. An approach called smart home energy management systems (SHEMS) is implemented to balance the power demand and the supply more effectively throughout the smart micro-grid by utilizing control algorithms simulated using MATLAB Simulink. This is achieved by utilizing real data from a home with commercial and residential load features in an urban area. The SHEMS revealed greater management and efficiency in electric power savings by reducing the utilities power demand. The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed. Key Words: Balance of Power Demand, Energy efficiency, Smart Distribution Board (SDB), Smart Home Energy Management System (SHEMS), Artificial Intelligence.

Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning

The paper “Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning” proposes a system that aims to optimize energy consumption in a smart home environment. The system uses Internet of Things (IoT) devices to collect real-time data on energy usage and machine learning algorithms to predict future consumption patterns. This paper proposes the use of deep neural networks (DNNs) for the design and implementation of a smart home energy management system using IoT and machine learning techniques. The authors demonstrate the effectiveness of the system through experimental results, showing significant energy savings compared to traditional methods. The DNN is built using Keras or Tensor Flow and is trained on extracted features from energy consumption data collected using IoT sensors. The system is implemented with a real-time monitoring system and a user interface for remote access. The proposed system has the potential to save energy and reduce energy costs for households while providing real-time feedback to the user.