Efficient Home Energy Management System Research Articles

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.

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.

Energy routing based optimal solution for residential peak power reduction with photovoltaic and electric vehicle penetration

ABSTRACT A customized design for an efficient home energy management system for peak power limiting and energy routing-based demand response strategy is proposed in this paper. The proposed system encompasses acquisition of historic data of residential load, photovoltaic (PV) generation and electric vehicle (EV) details, followed by load and solar power generation data forecast using the regression tree model. Gaussian distribution is used to model EV-related information, viz. arrival and departure schedule, SoC of EV batteries, etc. The predicted profiles are used in the adopted optimization process to estimate future energy balances. The presented optimization framework is a sequential process using whale optimization algorithm and fuzzy logic to schedule the connection time of residential appliances and knapsack algorithm for energy routing from additional energy sources, like rooftop solar photovoltaic systems and electric vehicles. Finally, the methodology is tested for its robustness and flexibility considering the actual data of a residential community having a variety of consumers. Promising results obtained validate the effectiveness of the proposed work with an average 27% reduction in peak power drawn, 30.69% in peak-to-average ratio, and 6.5% in the consumer electricity bill in the community.

Dynamic centralized control and intelligent load management system of a remote residential building with V2H technology

Energy is the main engine and the active element for all growth and development. It is the basic element for all sectors of the economy and a companion to human life. It is also not forgotten that most of the energy used in the whole world is traditional and unsustainable energy, as well as polluting the environment and causing harmful emissions. Sustainable is based primarily on environmental protection. Massive population growth is accompanied by a significant increase in energy consumption. Currently, most countries are turning to rely mainly on renewable energy sources. To achieve the maximum benefit from the energy, the tendency is to manage energy within the home to avoid the occurrence of energy losses. In this paper, an efficient home energy management system (HEMS) is proposed. This system is based on the use of a field-programmable gate array (FPGA) for home energy management. The proposed HEMS takes into account both generation and consumption as FPGA manages the feeding process through the sources. The prototype is implemented using Altera Cyclone IV EP4CE6 FPGA board using QUARTUS II software. The home is fed through PV, wind and fuel cells (FC) sources. If there is a shortage in energy production and the three sources are unable to cover the loads, the batteries enter into service to cover this shortage. If the sources and batteries are unable to cover the required loads, the EV contributes to covering the load, but with specific constraints set by the owner. The proposed HEMS works to control the feeding of the loads, as it schedules the feeding of the loads according to priority in the event of severe energy shortages and the inability of the sources, batteries and the EV to cover the loads. The experimental and simulation results proved the ability of the proposed system to respond to the demand by managing high energy-consuming household appliances according to the pre-set priority.

Modelling of Home Appliances Using Fuzzy Controller in Achieving Energy Consumption and Cost Reduction

While home energy prices keep rising, homeowners nowadays are searching for the right options to reduce their electricity bills. Besides, the increase in power consumption can contribute to environmental pollution. Therefore, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, the most domestic household appliances consumption of energy are modelled and analysed using the fuzzy logic controller (FLC) in order to permit the home energy management system (HEMS) to perform energy utilization estimation and cost analysis. These appliances are the heating ventilation and air conditioning (HVAC), electric water heater (EWH), and lighting, respectively. The developed system can help to analyse the appliances’ energy consumption and cost sceneries during peak and off-peak hours. The modelling of a fuzzy-based domestic appliances controller for HEMS takes the peak and non-peak tariff of Malaysian grid into consideration. The simulation results demonstrate that the developed models are able to manage energy consumption and cost reduction efficiently. By using the proposed FLC, the cost of energy is reduced by 21.75 %, 30.77 %, and 41.96 % for the HVAC, EWH, and dimmable lamps, respectively. In sum, the FLC shows good performance to reduce the cost and power consumption toward efficient HEMS.

Application of Two-Phase Simplex Method (TPSM) for an Efficient Home Energy Management System to Reduce Peak Demand and Consumer Consumption Cost

Superabundant utilization of electricity in the residential sector is one of the major reasons for frequent peak demand. Hence, power sector necessitates an appropriate solution to control and monitor the peak demand. In this regard, implementation of an appropriate home energy management system becomes mandatory at customer premises to have an effective control over peak demand. Thus, in this research a simple home energy management using Two-Phase Simplex Method (TPSM) is proposed with an objective to (i) reduce peak demand, (ii) reduce consumer consumption cost, and (iii) conserve consumer comfort level. Further, the research proposes detailed investigations on the smart energy-home management model monitored by IoT. For simulations, different load scenarios are considered and the results are compared with the existing benchmarks available in the literature. On validations, the proposed TPSM method is found simple, reliable and efficient. More importantly, the multipurpose objectives has certainly given better results in consumer consumption cost that can give better control to peak demand. Furthermore, the usage of lucid simplex method has almost reduced the computational complexity to fasten the response time. In this regard, consumer comfort is served here is considered as a major accomplishment with the proposed work.

Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances Towards Energy Saving and Cost Reduction Under Demand Response Consideration

Recently, homes consume around 40% of world power and produce 21% of the total greenhouse gas emissions. Thus, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, an intelligent home energy management system (HEMS) is developed to control domestic appliances load. The motivation of this work is reduced the electricity cost and power consumption from all the appliances by maintaining the customer’s high comfort level using an efficient optimized controller. The domestic household appliances such as heating ventilation and air conditioning (HVAC), electric water heater (EWH) and lighting were modelled and analysed using Simulink/Matlab. The developed models analysed the appliances’ energy consumption and cost sceneries during peak, off-peak and both peak and off-peak hours. Fuzzy logic controller (FLC) was developed for the HEMS to perform energy utilization estimation and cost analysis during these periods taking the Malaysian tariff for domestic use into consideration. To improve the FLC outcomes and the membership function constraint, particle swarm optimization (PSO) is developed to ensure an optimal cost and power consumption. The results showed that the developed FLC controller minimized the cost and energy consumption for peak period by 19.72% and 20.34%, 26.71% and 26.67%, 37.5% and 33.33% for HVAC, EWH, and dimmable lamps, respectively. To validate the optimal performance, the obtained results shows that the FLC-PSO can control the home appliances more significantly compared to FLC only. In this regard, the FLC-PSO based optimum scheduled controller for the HEMS minimized power and cost by 36.17%-36.54%, 54.54%-55.76%, and 62.5%-58% per day for HVAC, EWH, and light, respectively. In sum, the PSO shows good performance to reduce the cost and power consumption toward efficient HEMS. Thus, the developed fuzzy-based heuristic optimized controller of HEMS is beneficial towards sustainable energy utilization.

Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer

The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.

Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid

The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined objective load curve for electricity. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, concept of coordination among home appliances is presented. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the waiting time of the appliance. Whereas, electricity consumers have stochastic nature, for which, nature-inspired optimization techniques provide optimal solution. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user's demand. To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multi-objective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Also, test functions for convex, non-convex and discontinuous Pareto front are implemented to prove the efficacy of proposed techniques.

Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels

This paper presents an efficient home energy management system (HEMS) by optimal utilizing battery energy storage system (BESS) and photovoltaic (PV) systems. In the proposed HEMS, charging-discharging regime, capacity, and power of BESS are considered as design variables and optimally determined. Three operating conditions are considered for the home including: (i) home can receive energy from the network during off-peak low-cost hours, (ii) home can send energy to the main grid during on-peak high-cost hours for making the profit, and (iii) home can work on net-zero energy (NZE) model or standalone mode. The BESS is utilized to store energy during off-peak low-cost hours and discharge energy during on-peak high-cost hours. The proposed planning for determining the optimal operation strategy and sizing of BESS is expressed as a stochastic mixed integer nonlinear programming (MINLP). As well, output power produced by photovoltaic (PV) system is regarded as uncertain parameter and modeled by probability distribution function (PDF). Monte-Carlo Simulation (MCS) is applied to cope with uncertainties. The proposed stochastic MINLP is solved by Meta-heuristic optimization techniques. Simulation results demonstrate that the proposed HEMS can significantly reduce annual electricity bill. As well, NZE model can also be achieved by installing BESS and PV system at the same time.

MinNet: toward more intelligent smart home energy management systems with fewer sensors

The smart home technology has attracted considerable attention for decades. Nowadays, as the drastic increase of home energy consumption, efficient home energy management system HEMS has become a major building block. Aiming at building an easy-to-deploy intelligent HEMS, we design MinNet to i monitor the states of individual home appliances using the minimum number of power sensors and ii provide occupancy states of a house/room with inference intelligence. In detail, we use convenient plug-in power sensors to construct a wireless sensor network for data collection and establish an analytical framework based on the power model of appliances. By solving a carefully-designed optimisation problem, we find the minimum number of sensors to decode the states of appliances from their aggregated power and infer the occupancy states of the house/room. We implement MinNet in a house and validate its accuracy in appliance states monitoring and occupancy inference.

More efficient home energy management system based on ZigBee communication and infrared remote controls

This paper describes more efficient home energy management system to reduce power consumption in home area. We consider the room easily controllable with an IR remote control of a home device. The room has automatic standby power cut-off outlets, a light, and a ZigBee hub. The ZigBee hub has an IR code learning function and educates the IR remote control signal of a home device connected to the power outlet. Then the power outlets and the light in the room can be controlled with an IR remote control. A typical automatic standby power cut-off outlet has a waiting time before cutting off the electric power. It consumes standby power during that time. To eliminate the waiting time, we turn off the home device and the power outlet simultaneously with an IR remote control through the ZigBee hub. This method actively reduces the standby power. The proposed HEMS provides easy way to add, delete, and move home devices to other power outlets. When a home device is moved to the different outlet, the energy information of the home device is kept consistently and seamlessly regardless of location change. The proposed architecture gives more efficient energy-saving HEMS.