Residential Energy Management Research Articles

Multi-agent-based decentralized residential energy management using Deep Reinforcement Learning

In smart grid, energy consumption has grown exponentially in residential houses, which necessitates the adoption of demand response management. To alleviate and handle the energy management in residential houses, an efficient residential energy management (REM) system can be employed to regulate the energy consumption of appliances for several energy loads such as non-shiftable, shiftable, and controllable loads. Many researchers have focused on the REM using machine learning and deep learning techniques which is not able to provide secure and optimal energy management procedure. Thus, in this paper, a multi-agent-based decentralized REM, i.e., MD-REM approach is proposed using Deep Reinforcement Learning (DRL) with the utilization of blockchain. Furthermore, The combinatorial model DQN, i.e., Q-learning and deep neural network (DNN) is employed, to gain the optimal price based on the reduced energy consumption by appliances associated with different energy loads utilizing the Markov Decision Process (MDP). Here, multiple agents are designed to handle different energy loads and consumption is controlled by the DQN agent, then reduced consumption data is securely shared among all stakeholders using blockchain-based smart contract. The performance evaluation of the proposed MD-REM approach seems to be efficient in terms of reduced energy consumption, optimal energy price, reward, and total profit analysis. Moreover, blockchain-based result is evaluated for the proposed MD-REM approach considering the performance metrics such as transaction efficiency, Interplanetary File System (IPFS) bandwidth utilization, and data storage cost comparison.

Improving Usability in Mobile Apps for Residential Energy Management: A Hybrid Approach Using Fuzzy Logic

This paper presents a study that evaluates the usability and user experience of a mobile application interface for residential energy management, adopting a hybrid approach that integrates quantitative and qualitative methods within a user-centered design framework. For the evaluation, metrics and tools such as the User Experience Questionnaire Short (UEQ-S) and the System Usability Scale (SUS) were used, in addition to the implementation of a fuzzy logic model to interpret and contrast the data obtained through these metrics, allowing a more accurate assessment of usability and user experience, reflecting the variability and trends in the responses. Three aspects evaluated stand out: satisfaction with the interface, ease of use, and efficiency. These are fundamental to understanding how users perceive the system. The results indicate a high likelihood of user recommendation of the system and a high overall quality of user experience. This study significantly contributes to mobile application usability, especially in residential energy management, offering valuable insights for designing more intuitive and effective user interfaces on mobile devices.

Residential energy management with flexible and forecast uncertainties

Residential energy management with flexible and forecast uncertainties

A novel energy management method for multiple residential energy systems with energy exchange

Energy saving and emission reduction are the worldwide outstanding problems in the development of economy and society, which lead to wide attention of residential energy management. This paper presents a novel energy management method for multiple residential energy systems with energy exchange based on action-dependent heuristic dynamic programming (ADHDP). The major contributions are displayed as follows: (1) a novel energy management model is established for multiple residential energy systems with real-time electricity price and time-varying residential loads; (2) a novel ADHDP based energy management method is presented to obtain the energy scheduling scheme of multiple residential energy systems; (3) the continuous control action of the overall system is transformed into discrete control space which avoids training the action network. First, the structure and system models of multiple residential energy systems are introduced, and the energy management problem is formulated. Then, a brief introduction of ADHDP method is provided. Based on this, an energy management method is presented and an effective discretization method of control action is developed for solving the presented energy management problem with less computational burden. Simulation results show that the presented method can reduce the energy cost effectively. Finally, the comparison results verify the superiorities over the traditional energy management methods.

Stochastic Method for Generating Residential Household Energy Models of Varying Income Level and Climate Zone for Testing Energy Fairness

Abstract Recent developments in residential energy management necessitate testing of whether energy technology and policy fairly impact households of various income levels and locations. Considering income level is particularly important because increased utility bills put low-income households at higher risk of economic and health issues. This paper proposes a novel method for generating diverse household energy models of varying income levels and climate zones, which can be used to test the fairness impacts of residential energy developments. Models are stochastically generated using probability distributions based on data from national surveys. Included in the model are constant and time-variable features. Models capture the randomness inherent in the residential sector while still following realistic patterns of each income-climate group regarding building structure, appliance stock, and occupant behavior. Models were validated by comparing when, how, and how much energy is consumed by simulated versus real-life surveyed households. A total of 200 household models were simulated for the validation process, representing four climate zones and five income levels. Simulated energy consumption was plotted against survey data from the same income level, climate zone, and income-climate combination. Through correlation analysis and null hypothesis testing, it was determined that there is no statistically significant difference between simulated and surveyed energy consumption. For all cases considered, the correlation of the data is highly statistically significant. When validating all cases for income-climate classifications and individual climate zones, there was less than a 0.05% chance of uncorrelated data exhibiting such high correlation coefficients (r2), which ranged from 0.862 to 0.998. When validating models of each income level, this chance ranged from less than 0.05% to 0.194% with r2 values ranging from 0.816 to 0.984. A linear trendline was fitted to the data, and a null hypothesis test was performed to check if the slope statistically differed from 1. All cases tested resulted in a P value greater than 0.05 which, for a 95% confidence level, indicates that no significant difference can be determined. Because the plot of simulated versus survey data was very highly correlated and exhibited a slope statistically indistinguishable from 1, simulated household models were determined to represent real-life households with sufficient accuracy.

Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management

Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management

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>.

Real-time energy scheduling for home energy management systems with an energy storage system and electric vehicle based on a supervised-learning-based strategy

With rising energy costs and concerns about environmental sustainability, there is a growing need to deploy Home Energy Management Systems (HEMS) that can efficiently manage household energy consumption. This paper proposes a new supervised-learning-based strategy for optimal energy scheduling of an HEMS that considers the integration of energy storage systems (ESS) and electric vehicles (EVs). The proposed supervised-learning-based HEMS framework aims to optimize the energy costs of households by forecasting the energy demand and simultaneously scheduling the charging and discharging operations of ESS and EV. From the scenarios extracted from historical data, the HEMS optimization problem is solved using a mixed-integer linear programming (MILP) solver to collect the datasets on the optimal actions of the ESS and EV. Accordingly, a supervised learning method is used to learn the optimal actions of the MILP solver using deep neural networks (DNNs). Well-trained DNNs act as decision-making tools that are subsequently applied to predict near-optimal actions for ESS and EV based on real-time data. The effectiveness of the proposed method is demonstrated through simulation results and compared with deep reinforcement learning-based and forecasting-based methods. The results show that the proposed method can significantly reduce energy costs and improve the efficiency of ESS and EV operations. Overall, the proposed supervised-learning-based HEMS offers a practical and effective solution for residential energy management.

Graph representation learning-based residential electricity behavior identification and energy management

It is important to achieve an efficient home energy management system (HEMS) because of its role in promoting energy saving and emission reduction for end-users. Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy. However, current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications. This can lead to an insufficient description of behavior and suboptimal management strategy. To address these gaps, this paper proposes non-intrusive load monitoring (NILM) assisted graph reinforcement learning (GRL) for intelligent HEMS decision making. First, a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads. Thus, efficient identification of user behavior and description of state transition can be achieved. Second, based on the online updating of the behavior correlation graph, a GRL model is proposed to extract information contained in the graph. Thus, reliable strategy under uncertainty of environment and behavior is available. Finally, the experimental results on several datasets verify the effectiveness of the proposed model.

Optimizing residential DC microgrid energy management system using artificial intelligence

Optimizing residential DC microgrid energy management system using artificial intelligence

Smart residential distribution energy management system with integration of demand response and Aggregator

Smart residential distribution energy management system with integration of demand response and Aggregator

Design and fabrication of an IoT-based smart electrical meter for residential energy management

<p>Transparency in electricity measurement and efficient use of electricity are issues of concern to households. Electricity customers want to have confidence in their utility company's electricity meters installed at their houses. This study designs and constructs an internet of things (IoT)-based smart electric meter to measure electricity and remotely control domestic appliances. This device can monitor, calculate electricity bills and remotely control the appliances via the internet; electrical quantities are displayed in real-time on the Blynk app, liquid crystal display (LCD) screen and Google Sheet. The device is also capable of emailing the electricity user. The proposed system is designed and developed based on ESP32, a LCD, a PZEM 004T, four relays and a DC source. The results show that the device works stably and the device’s error is less than 5%. This research makes it possible for users to conveniently monitor their power consumption while enhancing their energy-saving awareness.</p>

On the trade-off between profitability, complexity and security of forecasting-based optimization in residential energy management systems

With the emergence of affordable access to data sources, machine learning models and computational resources, sophisticated control concepts for residential energy management systems (EMSs) are on the rise. At the heart of those are production and consumption forecasts. Given the wide spectrum of implementation opportunities, selection of appropriate forecasting strategies is challenging. This work systematically evaluates forecasting-based optimization for residential EMSs in terms of trade-offs between economic profitability, computational complexity and security. The foundation of the study is two real prosumer cases equipped with a photovoltaic-battery system. Results demonstrate that, within the considered scenarios, best trade-offs are achieved based on forecasts of a default gradient-boosted decision trees model, using a short initial training set, weather forecast inputs and regular retraining. Over 90% of the theoretical maximum economic benefit is achieved in this scenario, at significantly lower computational complexity than others with similar savings, while being applicable to new systems without large data history. In terms of security, this scenario exhibits tolerance against weather input manipulation. However, sensitivity to price tampering may require data integrity checking in residential EMSs.

Plugged-in electric vehicle-assisted demand response strategy for residential energy management

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip.

Reward Shaping-Based Actor–Critic Deep Reinforcement Learning for Residential Energy Management

Residential energy consumption continues to climb steadily, requiring intelligent energy management strategies to reduce power system pressures and residential electricity bills. However, it is challenging to design such strategies due to the random nature of electricity pricing, appliance demand, and user behavior. This article presents a novel reward shaping (RS)-based actor–critic deep reinforcement learning (ACDRL) algorithm to manage the residential energy consumption profile with limited information about the uncertain factors. Specifically, the interaction between the energy management center and various residential loads is modeled as a Markov decision process that provides a fundamental mathematical framework to represent the decision-making in situations where outcomes are partially random and partially influenced by the decision-maker control signals, in which the key elements containing the agent, environment, state, action, and reward are carefully designed, and the electricity price is considered as a stochastic variable. An RS-ACDRL algorithm is then developed, incorporating both the actor and critic network and an RS mechanism, to learn the optimal energy consumption schedules. Several case studies involving real-world data are conducted to evaluate the performance of the proposed algorithm. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art RL methods in terms of learning speed, solution optimality, and cost reduction.

A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

Distributed peer-to-peer transactive residential energy management with cloud energy storage

Transactive energy management stimulates residential prosumers to participate in a local market. This improves energy efficiency and reduces energy costs. An effective method to reduce consumers' electricity bills is the usage of energy storage devices. However, the high investment and maintenance costs of these devices still limit their applications in the individual distributed framework. Recently, cloud energy storage (CES) as a shared energy storage technology has been introduced to provide storage services for residential consumers at a lower cost. In order to overcome the limitations of the individual framework and create new economic prospects, the CES is used in this paper to support numerous residential consumers in the energy market. This paper proposes an optimal energy management model in a transactive energy framework based on a distributed optimization mechanism for peer-to-peer (P2P) energy trading with the usage of the alternating direction method of multipliers. In this cooperative structure, residential users and CES participate in a decentralized framework to reach a consensus that preserves their privacy. In this proposed model, different types of loads, distributed energy resources and users' costs consisting of the grid energy cost, the P2P energy cost, the battery degradation cost, the thermal discomfort cost, the interruption cost, and the daily investment cost for individual batteries and CES are considered. The effectiveness of the proposed model is verified through various simulations. Numerical results show that the presence of CES in the proposed model and its participation in P2P energy trading reduces the cost of residential users, decreases the energy demand from the network, and increases CES owner's profit. Therefore, the application of the proposed model is valuable for the residential users, CES, and the grid.

Incorporating coincidental water data into non-intrusive load monitoring

Non-intrusive load monitoring (NILM) is among successful approaches aiding residential energy management. However, the presence of multi-mode appliances and appliances with close power values and lack of a proper volume of training dataset have remained influential in worsening the computational complexity and diminishing the accuracy of classification-based NILM algorithms. To tackle these challenges, we propose a novel classification process, which considers the correlation of water and electricity consumption of some appliances as a novel signature in the network to improve the accuracy of disaggregation process in overlapping modes and tackle the lack of proper volume of training dataset. In the first phase of the proposed method, the K -nearest neighbors method, as a fast classification technique, is employed to extract power signals of appliances with exclusive non-close power values. Then, two different deep learning-based methods are proposed to disaggregate the consumption of appliances with close consumption values considering the correlation of electricity and water consumption of some appliances. Throughout these methods the water consumption of these appliances are also disaggregated. The main objectives of the proposed methods are increasing accuracy in the close modes of power consumption, and informing consumers about the water consumption pattern of some appliances. To illustrate the proposed processes and validate its effectiveness, the Almanac Minutely Power Dataset as a real dataset with a sampling rate of 1-minute is considered. The numerical results show marked improvement with respect to the existing classification-based NILM techniques. Moreover, it shows the applicability of proposed methods in dealing with low-frequency readings carried out by existing smart meters.

A novel energy management model among interdependent sections in the smart grids

AbstractTechnically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes.

Two-stage residential community energy management utilizing EVs and household load flexibility under grid outage event

To relieve the impact of the power grid outages on the residential sector, residential building energy management under grid outage events has been becoming the research hotspot in both the academic and industry. Considering the coordination of electric vehicles (EVs) and household load flexibility, this paper proposes a two-stage energy management approach for residential community under the planned outages. In the optimal scheduling stage, after receiving the information of the planned outage from the grid, the EVs’ charging–discharging power and community load curve reshaping schemes are optimal determined by residential community energy management system (CEMS), aiming at to minimize the total amount of unserved load of the whole community over the planned outage horizon. In the power allocation stage, with the formulated power allocation model, the determined residential community load curve from the above stage is then allocated to each house. The numerical test is finally conducted and the results validated the effectiveness and feasibility of the proposed approach.