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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. [ABSTRACT FROM AUTHOR]

Solar energy is a reliable and eco-friendly solution for power outages in Karbala, Iraq. This study presents a smart grid technology model for energy management in electrical systems, optimizing power schemes and economic benefits through a unique spatial distribution approach in Iraq, with the primary objective of ensuring consistent base loads for smart homes while achieving other economic goals. The algorithm's effectiveness was tested in three different scenarios. The energy was supplied by the national grid and battery bank-powered base loads. Meteorological data, including temperature and solar radiation, was gathered from a station in Karbala city for testing and evaluation. The study found that energy consumption decreased by 85% in April, with solar energy accounting for 37% of the total consumption. Smart homes saved 48% of energy, reducing reliance on the grid to 15%, as well as the reduction of energy consumption reached up to 47% and 60% in January and July, respectively, with solar energy estimated at 14% and 26% in those months. [ABSTRACT FROM AUTHOR]

As the Internet has become extensively utilized, developments in Internet-of-Things technologies have evolved to the point that researchers now recognize them to be cutting-edge achievements. In this work, an IoT-based smart home energy management system with multi-sensor data fusion enables online management and access of household utility and devices. The main control of the system will be a smart switch box that will oversee how much electricity and water are consumed. The potential of smart home energy management to connect and control appliances has grown and changed significantly in recent years. The suggested approach in this work is capable of gathering data for each object in our home, including operating durations and energy consumption statistics, as well as real-time tracking of the power consumed in it. The entire system is connected to the IoT Smart Box, and data are collected via IoT. The IoT-based application is used to monitor, analyze, and manage energy use. As a result, the energy loss caused by human negligence is reduced. This establishes itself as a full-fledged IoT-based smart home energy management system. [ABSTRACT FROM AUTHOR]

Home appliances connected to Internet-of-Things (IoT) platforms have been extensively installed in smart homes. In this context, home energy management systems (HEMSs) have emerged as a viable solution for reducing energy costs. Although several studies have analyzed the implementation of HEMSs, a majority of these studies were based on the installation of numerous sensors. Owing to the complexity and costs associated with the installation of multiple sensors, implementation of HEMSs in smart homes is challenging. This paper presents an energy management scheme for an HEMS that minimizes the energy cost in a smart home with data obtained from IoT-based appliances typically used in a smart home. Case studies were conducted to demonstrate the effectiveness of the proposed method. Prototype software (version 11) based on the proposed method for a HEMS and a test house with IoT-based appliances were also implemented in the case studies. [ABSTRACT FROM AUTHOR]

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 decisionmaking efficiency. [ABSTRACT FROM AUTHOR]

As a result of the exponential increase in energy consumption, energy shortages, and augmented energy costs have become a significant problem for households. Solar cells are used in many homes and/or buildings to address these problems. However, managing renewable energy sources in homes can be difficult due to the irregular nature of renewable energy production. Internet of Things (IoT) devices can provide real-time data on energy production and consumption, offering a promising solution to this issue. The current study proposes a framework based on IoT and blockchain technology for home energy management by predicting future energy consumption patterns and optimizing energy use in real time. The blockchain module facilitates peer-to-peer energy trading between renewable energy-generating homeowners and consumers. The proposed framework was tested employing a dataset based on smart homes with solar panels and wind turbines. The results manifest a reduction in energy costs and a possible 30% increase in their traditional gain. [ABSTRACT FROM AUTHOR]

End-User Development has been proposed over the years to allow end users to control and manage their Internet of Things-based environments, such as smart homes. With End-User Development, end users are able to create trigger-action rules or routines to tailor the behavior of their smart homes. However, the scientific research proposed to date does not encompass methods that evaluate the suitability of user-created routines in terms of energy consumption. This paper proposes using Machine Learning to build a Digital Twin of a smart home that can predict the energy consumption of smart appliances. The Digital Twin will allow end users to simulate possible scenarios related to the creation of routines. Simulations will be used to assess the effects of the activation of appliances involved in the routines under creation and possibly modify them to save energy consumption according to the Digital Twin's suggestions. [ABSTRACT FROM AUTHOR]

A smart home energy management system plays an important role in improving the efficiency of an energy distribution system and also helps to reduce the carbon footprint of the power utility company. For a developing country like India, one of the main challenges faced while integrating an energy management system and renewable energy technology is the migration cost faced by the user from the existing system. The existing energy policy of the nation or the community should be reformed in such a way that the user who is willing to adapt to an energy management system should be properly rewarded. Smart appliances and IoT-enabled devices reduce wiring complexity in any conventional home and the smart metering facility aids in the bidirectional communication between consumers and utility companies. But how does it take care of user privacy? What are the reasons behind the user's negligence on-demand response schemes in India? Through a case study, it was observed that the power consumption of domestic consumers in India increased over the years. It was also observed through an energy survey of 200 low-tension domestic consumers that a simple reengineering of lighting loads can save up to 4.68 Megawatt-hour of energy in a year. The paper also identified the negative impact of the inclining block rate billing scheme by comparing the bimonthly energy consumption pattern of consumers and also proposed a new billing scheme. The paper also reviews the types of optimization methods available for load scheduling. This paper is an attempt to enlighten readers on the importance of adopting a sustainable home energy management system, as a socio-economic commitment towards a green future. Highlights: The connected loads in Indian homes are increasing day by day and so the electricity bill A smart green HEM system helps to reduce the carbon footprint Novel demand response programs policies should be formulated Harvesting renewable energy will have multiple applications in a home Dimming lighting loads considerably reduces usage cost [ABSTRACT FROM AUTHOR]

The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments. [ABSTRACT FROM AUTHOR]

This study aims to develop an adaptable home energy management system capable of integrating the bidirectional smart charging of electric vehicles. The final goal is to achieve a user-defined objectives such as cost minimization or maximizing renewable self-consumption. Industrialwise, the present work yields valuable outcomes in identifying operational frameworks and boundary conditions. Optimal scheduling benefits both users and the electric network, thus enhancing grid utilization and increasing renewable energy integration. By coordinating power interactions with dynamic time-of-use tariffs, the energy management system minimizes user costs and aids the grid by cutting peak hour energy consumption. Charging and discharging operations in electric vehicles comply with energy level constraints outlined by bidirectional charging protocols. The proposed approach ensures the scheduling of cycles that minimize detrimental effects on battery health when evaluating an economically ageing mechanism. Compared to uncontrolled charging, optimal scheduling resulted in a significant reduction in the total operational cost of the dwelling. Trade-off conditions between renewable integration and potential savings are identified and numerically evaluated by means of multiobjective optimization. In contrast to scheduling-based models, the proposed architecture possesses the ability to iteratively adapt decision variables in response to system changes, thus responding effectively to external stochastic uncertainty. [ABSTRACT FROM AUTHOR]

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future. [ABSTRACT FROM AUTHOR]

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study's significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm's adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing. [ABSTRACT FROM AUTHOR]

Microgrids are local energy production and distribution networks that can operate independently when disconnected from the main power grid thanks to the integration of power generation systems, energy storage units and intelligent control systems. However, despite their advantages, the optimal energy management of real microgrids remains a subject that requires further investigation. Specifically, an effective management of microgrids requires managing a large number of electrical variables related to the power generated by the microgrid's power supplies, the power consumed by the loads and the aspects of power quality. This study analyzes how we can monitor different variables, such as the active power, reactive power, power factor, total harmonic distortion and frequency in the loads of a microgrid, using high-precision power meters. Our empirical study, conducted using a functional microgrid comprising a hybrid wind–solar power system and several household appliances, demonstrates the feasibility of using low-cost and high-performance power meters with IoT functionality to collect valuable power quality and energy consumption data that can be used to control the microgrid operation. [ABSTRACT FROM AUTHOR]

In this study, a flexible control approach is presented for smart home with DES system in microgrid connection. The smart home with DES has operation capability under GC‐Mode and I‐Mode functions with the proposed control algorithm. The control algorithm is also developed in terms of international standards IEEE 929 and IEC 61727 for grid connection requirements. Additionally, a PLL‐less control algorithm is embedded in the proposed control algorithm for the active/reactive power management and the grid synchronization. Thus, slow dynamic response and calculation burden issues in controller is avoided and the power flow capability of the grid‐connected DES in smart home is enhanced. Furthermore, the coordinate transformation is fulfilled for the power flow control in the control loop via the generation of cosine and sinus functions. Besides, the mathematical formulations of the proposed control algorithm are presented in detail. To show the effectiveness of the proposed control algorithm, a smart home with a photovoltaic DES model is designed and constructed in a simulation environment. The proposed control method is tested under various operation cases such as dynamic environmental conditions, GC‐Mode/I‐Mode, reactive power support and voltage variations. The proposed method shows efficient performance under applications of the different operation situations. Moreover, the international requirements are satisfied for GC‐Mode and I‐Mode transitions. [ABSTRACT FROM AUTHOR]

The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively. [ABSTRACT FROM AUTHOR]

In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer's daily activities based on actual time and next day's energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm's performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience. [ABSTRACT FROM AUTHOR]

Hydrogen plays a crucial role in the quest for sustainable and clean energy solutions, and its effect on smart home energy management is of particular interest. With the rapid advancements in smart home technologies, energy optimization has become essential, aiming to achieve efficient energy consumption, cost reduction, and enhanced user comfort. Green hydrogen, produced through the electrolysis of water using renewable energy sources, emerges as a promising solution for sustainable energy. It offers numerous benefits, including zero greenhouse gas emissions, high energy density, and versatile applications. In the context of this study, the enhanced northern goshawk optimization (ENGO) algorithm and the original northern goshawk optimization (NGO) algorithm are investigated for optimizing smart home energy management. By employing a two-stage approach based on high and low-velocity ratios, ENGO overcomes the limitations of NGO, such as low exploitation capability and being trapped in local optima. The study demonstrates that ENGO outperforms NGO in achieving multiple objectives simultaneously, including reducing the peak-to-average ratio (PAR), lowering electricity costs, and ensuring user comfort. Furthermore, ENGO proves to be more robust, capable of handling complex smart home energy management problems with multiple constraints. Thus, the integration of hydrogen solutions, such as green hydrogen, with advanced optimization techniques like ENGO, can significantly contribute to the effective management of energy resources in smart homes, promoting sustainability and user satisfaction. [ABSTRACT FROM AUTHOR]

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of 46.38 % was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) has a variety of application domains including smart homes. A smart home is an automated intelligent home where IoT technologies are used to remotely control home appliances, manage home energy, enhance home security, and increase the comfort of home residents. On the other hand, renewable energy resources are highly speared to cover huge electricity demand. To save more energy and maximize energy efficiency, smart homes are engaged with energy management systems. Energy management and optimization solutions can help in decreasing the overall cost of energy while optimizing all operational performance. This paper presents an IoT-based optimal energy management approach depending on the Harmony Search optimization technique to save energy usage in smart homes. PV and wind renewable energy systems are used to feed the home with electricity. These on-site energy sources help smart homes to decrease their dependence on power from the electricity grid. Energy storage systems are used to maintain energy system reliability due to the inherent randomness and intermittence of solar radiation and wind speed. The proposed algorithm depends on-demand response to the price variations in the electric energy over time and applies the Time-of-Use pricing principle for controlling household appliances. IoT technology is applied to exchange data between household appliances and the control center, in addition, to communicating both energy management systems and security systems with the control center. In this paper, the IoT system is based on ZigBee wireless technology which is described as the lowest power consumed wireless technology. The algorithm is applied to a proposed building consisting of five floors, each floor contains two apartments with a total area of 200 meters for one apartment. The obtained results prove the efficacy of the proposed Harmony Search-based optimization method in saving energy and reducing electricity bills in smart homes while satisfying the required constraints. Additionally, the performance of the proposed algorithm is validated against four AI algorithms including Genetic Algorithm, Artificial Immune System, Ant Lion Optimization, and Bat Algorithm. In addition to its simplicity in programming and formulation, the conducted comparison demonstrates a similarity in the results with an advantage in the proposed algorithm in improving both the electricity cost-saving and elapsed runtime. [ABSTRACT FROM AUTHOR]

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents an efficient model for optimal planning of electrical energy management in a smart residential microgrid (SRMG) by considering energy interactions among smart homes and with the purpose of SRMG's resilience improvement. A two-state linearized mathematical programming framework is given for the development of such notions. The first state proclaims a regular, disruption-free SRMG state, whereas the second state declares a disturbed SRMG state. In the initial state, two steps are taken into account. In the first stage, the minimum energy cost for each smart home, and in the second stage, minimum load profile deviation for SRMG is considered. In the second state energy interactions among in-home energy management systems to enhance the resilience is deliberated. The main objective of these two states is to advance SRMG's resiliency by considering modified SRMG's load profile and minimized smart homes' daily energy cost. Based on the obtained numerical results, energy interactions among smart homes have diminished smart home's energy cost about 3.34% in normal state, modified SRMG's load profile deviation about 3.62% in normal state, and improved SRMG's resilience about 32% in disrupted state. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The problem of scheduling household appliances with the availability of renewable energy is the biggest challenge in the smart home energy management system. The components such as renewable energy resources, household appliances, utility grid, storage batteries are pooled into a nonlinear, time-varying, indefinite, and dynamic structure that is impossible to control and refine. For this, real-time pricing is applied in most nations to withstand the burden on the grid. This requires attention to utilize renewable energy effectively. In this paper, a load scheduling method to schedule the loads based on the availability of solar energy and customer preferences is presented. First, the availability of solar energy is forecasted ahead one day using Regression Analysis. Second, the finite state machine approach-based load scheduling algorithm is implemented and tested using MATLAB Simulink and Lab VIEW. LabVIEW-based GUI is developed to visualize the MATLAB schedule for loads. The problem is divided into several states with the availability of solar power, and if solar power is unavailable, grid power is utilized. The loads preferred by the consumers are scheduled in alignment with the production of solar power with the finite state machine scheduling algorithm. Also, the loads considered are able to consume instantaneous energy with the instantaneous production of energy, thereby reducing CO2 emission by not consuming power from the grid. Finally, the loads are scheduled accordingly, and it is concluded that coordination can be established between energy providers, and the system proposed can flatten out the load profile. [ABSTRACT FROM AUTHOR]

An Internet of Things (IoT) device that can automatically measure water consumption can help prevent excessive water usage or leaks. However, automating too many residences or condominiums with multiple IoT devices can lead to extra energy consumption and more network congestion. We propose controlling the energy consumption of an IoT water consumption management system by dynamically controlling its duty cycle. By analyzing the energy consumption of the developed prototype and its duty cycle variation, we calculated how much energy could be saved by controlling the antenna and the water flow sensor used in the IoT device. While controlling the antenna offered some energy savings, having some way to cut down on the water flow sensor's consumption can have a dramatic impact on the overall IoT energy consumption or its battery longevity. Our results showed that we could get up to 69% extra energy savings compared to just putting the antenna in sleep mode. There is an observable trade-off in saving so much energy, as we can also see that water reading error rates go up alongside the extra energy savings. [ABSTRACT FROM AUTHOR]

Microgrids are local energy production and distribution networks that can operate independently when disconnected from the main power grid thanks to the integration of power generation systems, energy storage units and intelligent control systems. However, despite their advantages, the optimal energy management of real microgrids remains a subject that requires further investigation. Specifically, an effective management of microgrids requires managing a large number of electrical variables related to the power generated by the microgrid’s power supplies, the power consumed by the loads and the aspects of power quality. This study analyzes how we can monitor different variables, such as the active power, reactive power, power factor, total harmonic distortion and frequency in the loads of a microgrid, using high-precision power meters. Our empirical study, conducted using a functional microgrid comprising a hybrid wind–solar power system and several household appliances, demonstrates the feasibility of using low-cost and high-performance power meters with IoT functionality to collect valuable power quality and energy consumption data that can be used to control the microgrid operation.

This article presents an efficient home energy management system for residential household within a microgrid. The smart meter's database initially stores the consumer's solar generation, battery's state of charge, and appliance-level information. Both the appliance's consumption and its state of operation are gathered. The end-user's energy use habits are then investigated using this data. As a consequence, accurate data on the frequency of usage, preferred operation interval, and average power consumption of the appliances were gathered using a time-of-day schedule. The outcomes were integrated with a Competitive Price Tracking algorithm to produce a smart home energy management solution that is efficient and user-focused. In addition to providing the optimum energy management strategy for a smart home in a microgrid, the developed model planned the controllable loads by taking into account consumer comfort and a separate waiting factor for each Appliances. The suggested model is demonstrated via simulation in the Typhoon HIL Real-Time Simulator. The operating cost recorded from the simulation is 155.08 INR, 150.56 INR, and 130.54 INR for case 1, 2, and 3, respectively. However, the proposed method is compared to other method, where operating cost from NILM-based method is recorded as 155.08 INR,153.12 INR, and 142.98 INR for case 1,2, and 3, respectively, and 151.38 INR for all cases, recorded from traditional Method. [ABSTRACT FROM AUTHOR]

A real-time energy management system for an off-grid smart home is presented in this paper. The primary energy sources for the system are wind turbine and photovoltaics, with a fuel cell serving as a supporting energy source. Surplus power is used to generate hydrogen through an electrolyzer. Data on renewable energy and load demand is gathered from a real smart home located in the Yildiz Technical University Smart Home Laboratory. The aim of the study is to reduce hydrogen consumption and effectively utilize surplus renewable energy by managing controllable loads with fuzzy logic controller, all while maintaining the user's comfort level. Load shifting and tuning are used to increase the demand supplied by renewable energy sources by 10.8% and 13.65% from wind turbines and photovoltaics, respectively. As a result, annual hydrogen consumption is reduced by 7.03%, and the average annual efficiency of the fuel cell increases by 4.6% • Off-grid home energy management system using demand side control is proposed. • The system utilizes PV, WT, FC and an electrolyzer for green hydrogen production. • A FLC is used for real-time energy management and improved RES utilization. • Reduced hydrogen consumption and increased fuel cell efficiency is achieved. • Sustainable energy solution in remote areas without sacrificing user comfort. [ABSTRACT FROM AUTHOR]

Efficient energy management is greatly facilitated by accurately predicting energy consumption in a smart home, benefiting both consumers and utilities alike. The conventional forecasting techniques rely on pre-trained statistical models built upon extensive historical data, which may experience performance degradation due to the dynamic nature of power load demands. To address this limitation, this study proposes a novel approach employing Windowbased Support Vector Regression (WSVR) to accurately estimate energy requirements from a smart grid within a smart home. The dataset utilized for this research is sourced from Pecan Street in Texas, USA. To assess the efficacy of the proposed model, it is compared to several other time series data prediction models, including ARIMA, Holt Winter's, Linear Regression, Support Vector Machine, and Support Vector Regression. The performance of each model is evaluated, and the results are thoroughly examined and discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data's security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users' privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user's wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes. [ABSTRACT FROM AUTHOR]

• Metaheuristic Multi-Start algorithm for the demand consumption planning. • Determining the optimum PV installation sizing. • Nearly Zero Energy Building using photovoltaic energy. The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %. [ABSTRACT FROM AUTHOR]

In smart homes, the management of energy is gaining huge significance among researchers in recent times. This paper presents a system for predicting power utilization and scheduling household appliances in smart homes. The system utilizes a combination of Grey Wolf optimization (GWO), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) to improve energy management. The GWO algorithm is used to enhance the performance of the CNN-LSTM model. GWO is an optimization algorithm inspired by the hunting behaviour of grey wolves. It helps in finding optimal solutions for complex problems by mimicking the social hierarchy and hunting mechanisms of wolves. The fusion of CNN and LSTM serves as a pattern finding strategy for energy management. CNN is effective in extracting spatial features from data, while LSTM can capture temporal dependencies. By combining these two approaches, the model can analyze energy consumption patterns and make accurate predictions. To evaluate the performance of the proposed model, the paper uses three error metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The reported values of RMSE, MSE, and MAE are 0.6213, 0.3860, and 0.2808, respectively. These metrics indicate the accuracy of the model’s predictions, with lower values indicating better performance. Furthermore, this paper compares the proposed approach with the existing baseline models to access its superiority. According to the results, the proposed model outperforms the existing approaches in terms of prediction accuracy, as it achieves lower errors, compared to the baseline models. In summary, the proposed GWO-based CNN-LSTM network demonstrates improved prediction accuracy compared to the existing approaches, as indicated by the evolution metrics.

The residential sector contributes a large part of the energy to the global energy balance. To date, housing demand has mostly been uncontrollable and inelastic to grid conditions. Analyzing the performance of a home energy management system requires the creation of various profiles of real-world residential demand, as residential demand is complex and includes multiple factors such as occupancy, climate, user preferences, and appliance types. Average Peak Ratio (A2P) is one of the most important parameters when managing an efficient and cost-effective energy system. At the household level, the larger relative magnitudes of certain energy devices make managing this ratio critical, albeit difficult. Various Demand Response (DR) and Demand Side Management (DSM) systems have been proposed to reduce this ratio to 1. The main ways to achieve this are economic incentives, user comfort modeling and control, or preference-based. In this study, we propose a unique opportunistic social time approach called the Time Utility Based Control Feature (TUBCF), which uses the concept of a utility function from economics to model and control consumer devices. We propose a DR model for residential customers to reduce Peak-to-Average Ratio (PAR) and improve customer satisfaction by eliminating Appliance Wait Time (WTA) during peak periods. For PAR reduction and WTA, we propose a system architecture and mathematical formulation. Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics. Simulation results show that using this strategy can reduce A2P by 80% and improve user comfort during peak hours. [ABSTRACT FROM AUTHOR]

• This study proposes an hour-ahead demand response (DR) for PV-based smart homes utilizing the AI method in digital twin while taking into account customer energy consumption and discomfort; • This study adopts a MARL approach for ensuring optimum and decentralized decision-making among several devices; • This study presents a steady cost prediction method utilizing ANN for overcoming the uncertainties in upcoming costs; • This study also compares the energy prices under 2 various scenarios including and excluding AI-enabled DR. Energy management (EM) systems need to have the flexibility to take the optimum real-time decision in the face of constantly varying market factors. Demand response (DR) has become the newest method of improving the performance and reliability of the electrical system. Here, an hour-ahead DR algorithm is proposed for EM at home. This paper presents an artificial neural network approach that uses stable cost predictions as a method for dealing with upcoming price uncertainties. For making optimum and decentralized decisions for various household devices, multi-agent reinforcement learning has been used along with predicted upcoming costs. This paper conducts the simulation with shiftable, non-shiftable, and controllable loads to determine the effectiveness of this suggested EM strategy. Based on the outcomes of the experiments, this suggested DR algorithm has capable of handling EM for a number of devices, minimizing consumer electricity expenses and discomfort prices, and helping the consumer considerably reduce its energy expenses in comparison to a benchmark using no DR. [ABSTRACT FROM AUTHOR]

In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO 2 , noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16% and 99.97%, which proves the efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

In this paper, a smart home energy management system is proposed to improve the efficiency of the electricity infrastructure of residential buildings. To solve the scheduling problem of a smart building, we propose bacterial foraging ant colony optimization (HB-ACO). The primary objective of scheduling is to shift load from on-peak hours to off-peak hours to reduce electricity cost and peak-to-average ratio. A comparison of these algorithms is also presented in terms of performance parameters, electricity cost, reduction of PAR, and user comfort in terms of waiting time. The proposed techniques are evaluated using two pricing schemes: (1) time of use and (2) critical peak pricing. Moreover, coordination among home appliances is presented for real-time scheduling. We represent this as a knapsack problem and solve it through ant colony optimization algorithm. The HB-ACO shows better performance than ACO and BFA in reducing electricity cost, PAR, and increased user comfort, which is evident from the simulation results. [ABSTRACT FROM AUTHOR]

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy-consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens' engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854. [ABSTRACT FROM AUTHOR]

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications. [ABSTRACT FROM AUTHOR]

Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors. [ABSTRACT FROM AUTHOR]

Time-varying electricity prices in demand response (DR) programs motivate users to change their consumption behavior with the aim of electricity bill cost minimization. Coordinated DR schemes aim to flatten the aggregate load profile and mitigate rebound peaks. However, a comprehensive model which integrates supply and demand sides has to be developed to consider system constraints and renewable energy resources. In this regard, this article proposes a bi-level framework for supply and demand side energy management in an islanded microgrid. In the first level, consumers schedule their appliances’ operation to minimize their expenses. In the second level, a dynamic economic dispatch problem is solved which finds the power generation schedule for the generators resulting in the minimum electricity production cost. A game-theoretic turn-based decentralized algorithm is suggested which manages the interaction between both levels. Simulation results show that the proposed algorithm minimizes users’ electricity bill costs while satisfying power system constraints. [ABSTRACT FROM AUTHOR]

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer’s energy requirements. In many regions, energy utilization in the housing area is 30%–40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents’ actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches. [ABSTRACT FROM AUTHOR]

Near-Zero-Energy Buildings are a challenge in terms of energy production, storage, consumption and management, but these technological solutions remain financially difficult to access in developing countries. To this end, a complete low-cost and reliable home energy-management prototype was first developed and implemented on a scale model. A PWM charge controller drove the flow of energy produced and consumed in order to ensure the optimization of both the consumption of energy and energy savings. Battery storage was also managed by the home automation module using a set of sensors. The prototype of the scale model incorporated complete energy management of all electrical devices with group priorities through a graphical interface in a real-time mode. After testing this system, the lighting management part was implemented in a large-scale smart solar home. A smart lighting system via a complete algorithm integrated on an Arduino Mega board was then realized and implemented in the life-size house. This first step focuses, above all, on the users' comfort and, in particular, on the lighting management. The results show that this smart device thus makes it possible to achieve additional energy savings on an essential and yet already energy-efficient device: lighting. [ABSTRACT FROM AUTHOR]

In the past few years, the increase in the relation between the physical and digital world over the internet was witnessed. Even though the applications can enhance smart home systems, it is still early stages and challenges in the field of internet of things (IoT). An extreme level of data quality (DQ) system management is essential to produce a meaningful vision. However, in most home energy management system has no straightforward process of removing abnormal data. Hence, the research aims to propose and validate the model of anomaly detection for power consumption in real-time. The moving average (MA) approach identifies and removes abnormal energy consumption data. The results obtained from the forecasting time series auto regressive integrated moving average (ARIMA) model demonstrated that the proposed heuristics effectively enhanced energy usage forecasting. The selection of optimum parameter values for the MA was comprehended for time-series forecasting error minimization by comparing mean squared error (MSE). These outcomes proved the effectiveness of the existing technique and precision of choice of the appropriate. Therefore, the method can effectively route the cleaned sequence data streams in a real-time environment, which is valuable for spotting the anomalies and eliminating for enhancing energy usage time series. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range, two of the major concerns of BEV costumers. While the energy storing capabilities of BEVs usually exceed commuting requirements, batteries can be also utilized for home energy management system using bi-directional charging technology. This paper introduces an efficient energy management system for a smart home with BEVs and a bidirectional charger by addressing the corresponding optimal control problem of deciding the battery charging and discharging strategy to minimize energy expenditure and cost. To this aim, the electricity price and expenditure of upcoming weeks is forecasted using data from the present week, and a model of the complete system is used to find the optimal solutions by means of backward induction in a receding horizon approach. The proposed strategy relies on currently available information about the home and vehicle energy expenditure and energy prices in the recent past. The results of the study show a reduction of the electricity cost above 20% in the considered use-case. • Combined use of BEV and smart home for global energy consumption optimization. • Optimization of energy consumption considering hourly cost and power demand. • Backward induction technique embedded in MPC controller for energy optimization. • Reduction in electricity consumption costs by over 20% in a real application. [ABSTRACT FROM AUTHOR]