Home Energy Management System Using NILM and Low-Cost HAN

Home energy management systems （HEMs） are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network （HAN） that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in ~ecent years but they are still expensive enough to prohibit a mass scale deployments. In this paper, a very low cost alternative to the expensive HANs is presented. We have applied a combination of non-intrusive load monitoring （NILM） and very low cost one-way HAN to develop a HEM. By using NILM and machine learning algorithms we find the status of devices and their energy consumption from a central meter and communicate with devices through the one-way HAN. The evaluations show that the proposed machine learning algorithm for NILM achieves up to 99% accuracy in certain cases. On the other hand our radio frequency （RF）-based one-way HAN achieves a range of 80 feet in all settings.

A comprehensive analysis of smart home energy management system optimization techniques

Development of smart grid technology provides an opportunity to various consumers in context for scheduling their energy utilization pattern by themselves.The main aim of this whole exercise is to minimize energy utilization and reduce the peak to average ratio (PAR) of power.The two way flow of information between electric utilities and consumers in smart grid opened new areas of applications.The main component is this management system is energy management controller (EMC),which collects demand response (DR) i.e.real time energy price from various appliances through the home gateway (HG).An optimum energy scheduling pattern is achieved by EMC through the utilization of DR information.This optimum energy schedule is provided to various appliances via HG.The rooftop photovoltaic system used as local generation micro grid in the home and can be integrated to the national grid.Under such energy management scheme,whenever solar generation is more than the home appliances energy demand,extra power is supplied back to the grid.Consequently,different appliances in consumer premises run in the most efficient way in terms of money.Therefore this work provides the comprehensive review of different smart home appliances optimization techniques,which are based on mathematical and heuristic one.

Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.

Supply and Demand Oriented Energy Management in the Internet of Things

The Internet of Things (IoT) is emerging as an attractive paradigm involving physical perceptions, cyber interactions, social correlations and even cognitive thinking through a cyber-physical-social-thinking hyperspace. In this context, energy management with the purposes of energy saving and high efficiency is a challenging issue. In this work, a taxonomy model is established in reference to the IoT layers (i.e., sensor-actuator layer, network layer, and application layer), and IoT energy management is addressed from the perspectives of supply and demand to achieve green perception, communication, and computing. A smart home scenario is presented as a case study involving the main enabling technologies with supply-side, demand-side, and supply-demand balance considerations, and open issues in the field of IoT energy management are also discussed.

一种基于实时电价的HEMS家电最优调度方法

提出了一种应用于家庭能量管理系统(HEMS)的基于实时电价的家电最优调度模型。该模型采用线性整数规划方法建模,分别以用电费用最省、用电费用和满意度兼顾、二氧化碳排放最少为优化目标,以用户指定的运行时间要求以及电力公司的需求响应为约束条件。该模型将家庭中可以进行调度的可中断运行电器和不可中断运行电器,以及不可调度而必须运行的电器统一考虑,同时考虑使用家庭光伏(photovoltaic,PV)发电系统并允许剩余电力上网出售。提出的调度模型不仅考虑了家庭用电的各种实际情况,而且考虑了将来电网中可再生新能源的使用以及需求响应等新技术的应用,具有重要的实际意义。最后通过仿真实验验证了提出方法的有效性。

一种改进的HEMS家电最优调度方法

在现有的家庭能量管理系统(home energy management system,HEMS)的基础上增加分布式储能模块组成新的HEMS,并在此基础上提出了一种改进的基于0-1线性整数规划方法的家电最优调度模型。通过此调度模型,用户可以根据各自需求分别实现用电费用最省、用电费用最省同时兼顾满意度或者二氧化碳排放最小的目标。该调度模型无论是在目标函数还是在约束条件上都采用线性化表示的方法,在使用极短的调度时间的同时能够保证调度结果是最优结果。最后通过仿真实验验证了提出方法的有效性以及所提方法能够很好地应对电力公司的削峰填谷要求,具有重要的实际应用价值。实验结果表明,所提方法能够比以往相关研究取得更好的节约费用、减少二氧化碳排放的效果。

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

A Novel Model of Intelligent Electrical Load Management by Goal Programming for Smart Houses, Respecting Consumer Preferences

Energy management is being highly regarded throughout the world. High-energy consumption in residential buildings is one of the dominant reasons of excessive energy consumption. There are many recent works on the demand-side management (DSM) and smart homes to keep control on electricity consumption. The paper is an intelligence to modify patterns, by proposing a time scheduling consumers, such that they can maintain their welfare while saving benefits from time varying tariffs;a model of household loads is proposed;constraints, including daily energy requirements and consumer preferences are considered in the framework, and the model is solved using mixed integer linear programming. The model is developed for three scenarios, and the results are compared: the 1st scenario aims Peak Shaving;the 2nd minimizes Electricity Cost, and the 3rd one, which distinguishes this study from the other related works, is a combination of the 1st and 2nd Scenarios. Goal programming is applied to solve the 3rd scenario. Finally, the best schedules for household loads are presented by analyzing power distribution curves and comparing results obtained by these scenarios. It is shown that for the case study of this paper with the implementation of 3rd scenario, it is possible to gain 7% saving in the electricity cost without any increasing in the lowest peak power consumption.

A Dual-spline Approach to Load Error Repair in a HEMS Sensor Network

In a home energy management system(HEMS),appliances are becoming diversified and intelligent,so that certain simple maintenance work can be completed by appliances themselves.During the measurement,collection and transmission of electricity load data in a HEMS sensor network,however,problems can be caused on the data due to faulty sensing processes and/or lost links,etc.In order to ensure the quality of retrieved load data,different solutions have been presented,but suffered from low recognition rates and high complexity.In this paper,a validation and repair method is presented to detect potential failures and errors in a domestic energy management system,which can then recover determined load errors and losses.A Kernel Extreme Learning Machine(K-ELM)based model has been employed with a Radial Basis Function(RBF)and optimised parameters for verification and recognition;whilst a Dual-spline method is presented to repair missing load data.According to the experiment results,the method outperforms the traditional B-spline and Cubic-spline methods and can effectively deal with unexpected data losses and errors under variant loss rates in a practical home environment.

Hybrid-integer algorithm for a multi-objective optimal home energy management system

Most of the energy produced in the world is consumed by commercial and residential buildings.With the growth in the global economy and world demographics,this energy demand has become increasingly important.This has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management.This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing.This simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of appliances.The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer preferences.For optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm.Two techniques are proposed to manage the trade-off between the conflicting objectives.The first technique is the Pareto-optimal solutions classification using supervised learning methods.The second technique is called the lexicographic method.The simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in Python.The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio(PAR)by 28% and 49.32%,respectively,for the TOU tariff rate,and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.

Power Scheduling with Max User Comfort in Smart Home:Performance Analysis and Tradeoffs

The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy.Here,we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm(GWA)and Harmony Search Algorithms(HSA).Moreover,a fusion initiated on HSA and GWA operators is used to optimize energy intake.Furthermore,many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge.Hybridization has proven beneficial in achieving numerous objectives simultaneously,decreasing the peak-to-average ratio and power prices.Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method,Harmony GWA(HGWA).The simulations are for a multifamily housing complex outfitted with various cool gadgets.The simulation results indicate that GWA functions better regarding cost savings than HSA.In reputes of PAR,HSA is significantly more effective than GWA.The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR,as opposed to 503.4 in GWA,398.10 in HSA and 640.3 in HGWA.The suggested approach performed better than HSA and GWA in PAR reduction.For single-family homes in HGWA,GWA and HSA,the reduction in PAR is 45.79%,21.92%and 20.54%,respectively.The hybrid approach,however,performs better than the currently used nature-inspired techniques in terms of Cost and PAR.

New smart home energy management systems based on inclining block-rate pricing scheme

There are wide applications of block-rate pricing schemes in many countries.However,there are no significant studies that apply this common tariff for smart home energy management systems.In this paper,a three-time-frame energy management scheme has been proposed for photovoltaic(PV)-powered grid-connected smart homes based on the well-known mixed-integer linear programming optimization technique.This paper provides three original and novel smart home energy management algorithms that depend on the most common residential tariff specifically in developing countries.Three different management concepts have been studied for a typical Egyptian house.The concepts of shifting load,vehicle-to-home and reducing air conditioning have been tested according to a commonly applied slab tariff.The proposed scheme considers the home battery extending lifetime constraints.It also preserves comfortable lifestyle limits for home users according to Arab housing climatic conditions and culture.Moreover,the economic feasibility of integrated PV modules for the studied home has been verified according to the Egyptian tariff.The proposed energy management scheme of PV-powered home reduces the electrical power bill significantly in a wide range from 61%to only 19%of the default case bill according to the applied management technique.

基于模型预测控制的家庭能量管理优化调度方法研究

在分布式可再生能源大规模接入和分时电价实施的背景下,为降低电费成本、提高用户舒适度并提高可再生能源消纳率,提出了一种基于模型预测控制的家庭能量管理策略。建立由分布式光伏和各类用电负载等组成的家庭能量系统,分析各类设备的工作特性,提出相应的舒适度评价指标,特别针对空调这一典型功率可变负荷,结合建筑的热动态特性,建立室内温度预测模型。在建立家庭能量系统的基础上,使用遗传算法进行优化管理,并在模型预测控制框架下不断执行和更新。最后,实验对比结果表明,文中提出的基于模型预测控制的家庭能量管理策略可以有效实现能量的优化调度,并在预测不确定场景下具有较强鲁棒性。

几种家用大功率负荷精细化建模研究

该文基于家用负荷的物理模型、用户的使用习惯和用户的舒适度要求,提出了家用大功率负荷的精细化建模方法,该方法可应用于家庭能量管理系统,具有良好的应用前景。首先,建立考虑电热水器负荷的精细化热力学动态模型,考虑气温、墙体等因素的空调房间的精细化热力学动态模型和电动汽车负荷的精细化储能模型;然后,建立这三种负荷的精细化控制模型,通过自定义家庭算例将不同个性化的精细化参数模型对比分析。MATLAB仿真结果表明,该文建立的精细化动态模型能够准确描述用户的用电规律,对用户积极参与到电力系统需求响应中去起到促进作用。

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 insuffi-cient 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 uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.

一种基于用户通勤行为的家庭能量管理优化策略

随着电动汽车快速发展,V2G技术可大幅降低家庭能量管理系统中用户的用能成本,但V2G会影响用户出行。针对此问题提出了一种基于用户通勤行为的家庭能量管理系统优化策略,通过极大似然估计和蒙特卡罗模拟构建用户出行模型,其次将杂交粒子群与混沌算法、免疫算法相融合,利用多重混沌免疫杂交粒子群算法(MCIHPSO)对目标函数进行求解,最后,通过仿真及实验验证了本文所提控制策略显著降低V2G功能对用户出勤的影响。

Optimization Strategy Based on Deep Reinforcement Learning for Home Energy Management

With the development of a smart grid and smart home,massive amounts of data can be made available,providing the basis for algorithm training in artificial intelligence applications.These continuous improving conditions are expected to enable the home energy management system(HEMS)to cope with the increasing complexities and uncertainties in the enduser side of the power grid system.In this paper,a home energy management optimization strategy is proposed based on deep Q-learning(DQN)and double deep Q-learning(DDQN)to perform scheduling of home energy appliances.The applied algorithms are model-free and can help the customers reduce electricity consumption by taking a series of actions in response to a dynamic environment.In the test,the DDQN is more appropriate for minimizing the cost in a HEMS compared to DQN.In the process of method implementation,the generalization and reward setting of the algorithms are discussed and analyzed in detail.The results of this method are compared with those of Particle Swarm Optimization(PSO)to validate the performance of the proposed algorithm.The effectiveness of applied data-driven methods is validated by using a real-world database combined with the household energy storage model.

Integrated Optimization of Smart Home Appliances with Cost-effective Energy Management System

Smart grid enables consumers to control and sched-ule the consumption pattern of their appliances,minimize energy cost,peak-to-average ratio(PAR)and peak load demand.In this paper,a general architecture of home energy management system(HEMS)is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residen-tial customers.The optimization problem is developed under the time of use pricing(TOUP)scheme.To optimize the formulated problem,a powerful meta-heuristic algorithm called grey wolf optimizer(GWO)is utilized,which is compared with particle swarm optimization(PSO)algorithm to show its effectiveness.A rooftop photovoltaic(PV)system is integrated with the system to show the cost effectiveness of the appliances.For analysis,eight different cases are considered under various time scheduling algorithms.

Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management

This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.