Home Energy Management Algorithm Research Articles

A Case Study of Load Scheduling For Home Energy Management with Integrated Renewable Energy

Smart grids are very comprehensive systems where each sub-unit from generation to consumption should be considered separately. Home energy management systems and demand-side load management applications are also among the most important issues of smart grid systems. In this study, a smart home energy management model was developed in this concept, and energy management solutions were presented through an exemplary model. For this purpose, a home energy management algorithm was developed and simulated in the MATLAB Simulink environment by taking an apartment in Esenler, Istanbul as a reference. The smart home model discussed in this simulation study can generate its own electricity with renewable energy sources, store excess electrical energy in battery groups and also sell the surplus to the grid. This home model also enables end-users to control peak loads and schedule home appliances, especially during peak hours, following a demand-response program to consume energy more efficiently. Then, in order to see the electricity consumption results, electricity bill calculations were made according to both single and triple tariff pricing. The benefits of this model to the consumer and the grid were investigated, and also its effects on efficiency were examined. The results are given comparatively and the consumer’s saving is depicted in figures.

Residential energy management strategy considering the usage of storage facilities and electric vehicles

This article presents a different type of home energy management algorithm for a residence consisting of solar PV, battery storage units, electric vehicles with different driving profiles, critical and noncritical loads. The primary objective of the proposed methodology is to reduce the overall power purchase from the utility. In order to achieve the said objectives a combination of the rule based approach proposed by the authors and a heuristic optimization is used. Hence, it can be said that the proposed strategy consists of two layers, of which the first part of the algorithm schedules the noncritical loads using electric springs and the second part of the strategy controls battery storage units and electric vehicles. The novelty of the proposed work lies in using electric springs, battery storage units and exploring the virtual energy storage capabilities of electric vehicles so as to make the residential complexes more autonomous and less dependent on the utility. The robustness of the proposed strategy is tested on a residence and from the obtained results it can be said that the presented algorithm can save an electricity bill of about 53% to the consumer.

Home Energy Management Algorithm Based on Deep Reinforcement Learning Using Multistep Prediction

In recent years, home energy management systems (HEMS), which enable the automatic control of electrical equipment and home appliances, have been attracting attention as a method for saving electricity at home. HEMS achieve energy saving by visualizing energy consumption at home and controlling energy consuming equipment such as air conditioners. The optimum control law is difficult to attain, owing to uncertainties related to power demand and power supply from the electrical equipment. Deep reinforcement learning has been used to address energy optimization problems for home environments. However, in HEMS, several components such as heating, ventilation, and air conditioning (HVAC) systems, storage batteries, and electric water heaters are simultaneously controlled, and therefore, the action space becomes extremely large. Therefore, it may not be feasible to fully learn the rare experience using traditional deep reinforcement learning methods due to the large size of the state-action space and slow propagation of delayed rewards. In this study, we propose an energy management algorithm that uses the Dual Targeting Algorithm to strongly learn the experience of acquiring high returns using the quick propagation of delayed rewards via multistep returns. The proposed energy management algorithm is applied to a HEMS learning experiment to control a storage battery and an HVAC system, and its performance is compared to that of a Deep Deterministic Policy Gradient-based energy management system. As a result, it is confirmed that the proposed method can reduce the number of hours deviating from the comfort temperature range by about 17% compared to the existing method.

Wireless Home Energy Management System with Smart Rule-Based Controller

Despite the increasing utilization of renewable energy resources, such as solar and wind energy, most residential buildings still rely on conventional energy supply by public utility services. Such utility services often use time-of-use energy pricing, which compels residential consumers to reduce their energy usage. This paper presents a wireless home energy management (HEM) system that enables the automatic control of home appliances to reduce energy consumption to assist such energy users. The system consists of multiple smart sockets that measure the energy that is consumed by the connected appliances and are capable of implementing on/off commands. The system includes other support components for supplying data to a central controller, which utilizes a rule-based HEM algorithm. The control rules were designed, such that the lifestyle of the user would be preserved while the energy consumption and daily energy cost were reduced. The experimental results showed that the central controller could effectively receive data and control multiple devices. The system was also found to afford significant reductions of 23.5 kWh and $2.898 in the total daily energy consumption and bill of the considered household setup, respectively. The proposed HEM system promises to be particularly useful for households with a high daily energy consumption.

Experimental determination of ZIP coefficients for residential appliances and ZIP model based appliance identification: The case of YTU Smart Home

This study presents the experimental determination of the ZIP (constant impedance (Z), constant current (I), constant power (P)) coefficients for residential appliances in a smart home at Yildiz Technical University (the YTU Smart Home) in Istanbul, Turkey. First, active and reactive power consumptions of house appliances are measured with respect to the voltage change between 100–240V with a 10V increment. Then, Least Square Algorithm is used to calculate ZIP coefficients of household appliances. Using the obtained ZIP coefficients and an assumed appliance usage pattern, a daily residential active and reactive power demand profiles are obtained. Data from the ZIP models are compared with actual measurement data. It is shown that the ZIP models are quite accurate within 200–220V voltage range and can be used in generating residential demand profiles for system level analyses. In addition, the ZIP model based appliance identification algorithm is proposed to identify the plugged appliance in the smart home environment for better home energy management algorithm implementation.

Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances.

This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management systems, the novelty of the proposed approach is as follows: (1) a model-free Q-learning method is applied to energy consumption scheduling for an individual controllable home appliance (air conditioner or washing machine), as well as the energy storage system charging and discharging, and (2) the prediction of the indoor temperature using an artificial neural network assists the proposed Q-learning algorithm in learning the relationship between the indoor temperature and energy consumption of the air conditioner accurately. The proposed Q-learning home energy management algorithm, integrated with the artificial neural network model, reduces the consumer electricity bill within the preferred comfort level (such as the indoor temperature) and the appliance operation characteristics. The simulations illustrate a single home with a solar photovoltaic system, an air conditioner, a washing machine, and an energy storage system with the time-of-use pricing. The results show that the relative electricity bill reduction of the proposed algorithm over the existing optimization approach is 14%.

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and “reshape” the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system.

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Energy saving algorithm for smart home energy consumption budget optimization under the SVM model using a simple particle swarm optimization algorithm to find the optimal value caused by the slow speed gradient value based on PSO algorithm is proposed to optimize the use of machine learning algorithm GDPSO algorithm (Gradient Descent based on down Particle Swarm Optimization Algorithm). First of all, the establishment of energy structure and energy consumption model of optimal hyperplane selection of penalty factor and Gauss the appropriate parameters; secondly, parameter selection and optimization of energy consumption model for smart home energy-saving emission reduction requirements using GDPSO algorithm, and improves the efficiency of the optimal SVM parameters to improve the accuracy of solution. A simulation example shows the effectiveness of the algorithm.

A home energy management algorithm in demand response events for household peak load reduction

A home energy management algorithm in demand response events for household peak load reduction

HEM algorithm based smart controller for home power management system

The home power management system is proposed with the objective to reduce the electricity cost and also to avoid the problem of high peak demand. Recently more methods have been discussed in the area of Home energy management, but prioritizing the operation of power units from customer point of view has its own benefits depending on the comfort level. The Proposed home consists of a smart electrical appliance, photovoltaic system with battery, smart communication network and a robust controller. This controller schedules the power units in response to electricity price at the Time of Use (ToU). The available power units comprising of solar power, battery power, grid supply and the utilization of home appliance are categorized and monitored regularly. The Primary power units, preferably solar are chosen automatically as per the priority of the customer. When the primary power unit (solar) is not able to supply power, due to its intermittent nature of generation, the controller shifts to next power units accordingly. The simulation results show that the proposed system based on Home Energy Management (HEM) algorithm reduces the electricity cost, peak demand problem and enhances the efficiency of energy use. The Time of Use (ToU) is considered for reducing the peak demand. The smart controller is operated based on HEM algorithm and selects the power units accordingly. Also, there is a necessity of Energy conversion from DC (solar) to AC (grid/appliance), there is feasibility of power quality disturbance. This quality of power is improved by using Selective Harmonic elimination (SHE) method. The proposed system is developed and simulated in MATLAB/SimPowerSystem (SPS).

A new home energy management algorithm with voltage control in a smart home environment

Energy management in electrical systems is one of the important issues for energy efficiency and future grid systems. Energy management is defined as a HEM (home energy management) on the residential consumer side. The HEM system plays a key role in residential demand response applications. In this study, a new HEM algorithm is proposed for smart home environments to reduce peak demand and increase the energy efficiency. The proposed algorithm includes VC (voltage control) methodology to reduce the power consumption of residential appliances so that the shifting of appliances is minimized. The results of the survey are used to produce representative load profiles for a weekday and for a weekend. Then, case studies are completed to test the proposed HEM algorithm in reducing the peak demand in the house. The main aim of the proposed HEM algorithm is to minimize the number of turned-off appliances to decrease demand so that the customer comfort is maximized. The smart home laboratory at Yildiz Technical University, Istanbul, Turkey is used in case studies. Experimental results show that the proposed HEM algorithm reduces the peak demand by 17.5% with the voltage control and by 38% with both the voltage control and the appliance shifting.

Score‐based intelligent home energy management (HEM) algorithm for demand response applications and impact of HEM operation on customer comfort

A home energy management (HEM) system plays an important role for residential demand response (DR) applications in the smart grid environment. Its role includes the ability to manage end-use electrical energy (kWh) and demand (kW) based on utility signals, load priorities and customer preference. This study presents a score-based intelligent HEM algorithm for residential DR applications and the impact of the HEM system operation on customer comfort in various HEM decision-making intervals (i.e. 1, 5, 10 and 15 min) at various DR requirements. The proposed algorithm manages selected household loads according to customers' preset comfort level settings and keeps the total household power consumption below a certain limit. Results indicate that the more stringent DR requirements lead to the higher level of customer comfort violations during a DR event. In addition, the longer HEM decision-making intervals may lead to the higher level of customer comfort violations.

An Algorithm for Intelligent Home Energy Management and Demand Response Analysis

A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.