Smart Home Energy Management Research Articles

Research for Control Strategy of Smart Home Energy Management System with Distributed Photovoltaic Generation and Application Practice

Research for Control Strategy of Smart Home Energy Management System with Distributed Photovoltaic Generation and Application Practice

A Solution Based on Bluetooth Low Energy for Smart Home Energy Management

The research and the implementation of home automation are getting more popular because the Internet of Things holds promise for making homes smarter through wireless technologies. The installation of systems based on wireless networks can play a key role also in the extension of the smart grid towards smart homes, that can be deemed as one of the most important components of smart grids. This paper proposes a fuzzy-based solution for smart energy management in a home automation wireless network. The approach, by using Bluetooth Low Energy (BLE), introduces a Fuzzy Logic Controller (FLC) in order to improve a Home Energy Management (HEM) scheme, addressing the power load of standby appliances and their loads in different hours of the day. Since the consumer is involved in the choice of switching on/off of home appliances, the approach introduced in this work proposes a fuzzy-based solution in order to manage the consumer feedbacks. Simulation results show that the proposed solution is efficient in terms of reducing peak load demand, electricity consumption charges with an increase comfort level of consumers. The performance of the proposed BLE-based wireless network scenario are validated in terms of packet delivery ratio, delay, and jitter and are compared to IEEE 802.15.4 technology.

Optimal joint scheduling of electrical and thermal appliances in a smart home environment

With the development of home area network, residents have the opportunity to schedule their power usage in the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, a home energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented in which electrical and thermal appliances are jointly scheduled. The proposed method aims at minimizing the electricity cost of a residential customer by scheduling various type of appliances considering the residents consumption behavior, seasonal probability, social random factor, discomfort index and appliances starting probability functions. In this model, the home central controller receives the electricity price information, environmental factors data as well as the resident desired options in order to optimally schedule appliances including electrical and thermal. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, photovoltaic panel, combined heat and power unit, boiler and electrical and thermal storages over a 24-h period. The results show that the scheduling of different appliances can be reached simultaneously by using the proposed formulation. Moreover, simulation results evidenced that the proposed home energy management model exhibits a lower cost and, therefore, is more economical.

HARDWARE PROTOTYPE OF SMART HOME ENERGY MANAGEMENT SYSTEM

The development of ‘demand-side load management’ is the outcome of the smart grid initiative. Due to the significant amount of loads in the residential sector, home energy management has received increasing interest. In the country like India, we are lagging behind, in the power sector as the demand is much more than the supply. Moreover, there is not a single initiative, which has been taken for the deployment of smart suppliers and smart users. Here, I propose a hardware design of smart home energy management system (SHEMS). With the help of this proposed design, it is possible to have a real-time, price-responsive control strategy for domestic loads such as electrical water heater (EWH), illumination (Lights), air conditioning (Fan), dryer etc. Consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. This system is designed with sensors to detect human activities and the behavior is predicted by applying a machine learning algorithm in order to help consumers reduce total payment on electricity. Finally, for the verification of the hardware system, simulation and experiment results will be checked based on an actual SHEMS prototype.

Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle

One of the most challenging problems associated with operation of smart micro-grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multiobjective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensure an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data, and the obtained results were compared in terms of total energy consumption cost, users' convenience rates, and thermal comfort level.

Smart home energy management system including renewable energy based on ZigBee and PLC

As home energy use is increasing and renewable energy systems are deployed, home energy management system (HEMS) needs to consider both energy consumption and generation simultaneously to minimize the energy cost. This paper proposes a smart HEMS architecture that considers both energy consumption and generation simultaneously. ZigBee-based energy measurement modules are used to monitor the energy consumption of home appliances and lights. A PLC-based renewable energy gateway is used to monitor the energy generation of renewable energies. The home server gathers the energy consumption and generation data, analyzes them for energy estimation, and controls the home energy use schedule to minimize the energy cost. The remote energy management server aggregates the energy data from numerous home servers, compares them, and creates useful statistical analysis information. By considering both energy consumption and generation, the proposed HEMS architecture is expected to optimize home energy use and result in home energy cost saving.

PLC-based photovoltaic system management for smart home energy management system

As energy consumption in residential areas is rising, residential homes have deployed a photovoltaic (PV) system to save energy cost. The PV system needs to be continuously monitored to maintain its appropriate performance. In addition, it is desirable to monitor each PV module because one abnormal PV module affects the whole PV system. In this paper, the power line communication (PLC) compliant with HomePlug is adopted to monitor each PV module. The system architecture is composed of three components: PLC modem, renewable energy gateway (REG), and smart device application. The PLC modems are deployed on each PV module and monitor it. The REG aggregates and stores the monitored data from the PLC modems and the inverter. The smart device application retrieves the stored data from the REG to inform users of the status of the PV system. Users can browse and figure out the PV system performance in detail. The proposed system is installed in the field that is composed of sixteen 210W PV modules and a 3kW inverter. This scheme will maintain the performance of a PV system and contribute to enhancing home energy management system.

The Smart Grid—State-of-the-art and Future Trends

This paper introduces Smart Grid and associated technical, environmental and socio-economic, and other non-tangible benefits to society, and articulates the need for the concept and the fact that it is a dynamic interactive, real-time infrastructure that responds to the challenges of designing and building the power system of the future, rather than being simply a marketing term. To illustrate the diversity of terminology, the paper compares an Electric Power Research Institute (EPRI) definition with that suggested by a study group of the International Electrotechnical Commission (IEC). Next, a paper sponsored by the Canadian Electricity Association (CEA) that cites three example definitions to highlight the diversity of views of Smart Grid is briefly reviewed. Early misconceptions and characterizations of Smart Grid are discussed as a prelude to addressing challenging issues that motivate developing and implementing related innovative technologies, products and services. The paper then discusses the potential promise of the Smart Grid, which is embedded in its often-cited attributes of efficiency, accommodating, quality focus, enabling and self-healing to name some. The paper then addresses some of the often-cited impediments to accepting Smart Grid which are based on concerns and issues confronting its forward progress, adoption and acceptance. Distribution Automation (DA) and embedded intelligence are discussed emphasising self-healing, optimizing operation and facilitating recreation and recovery from abnormal events. Functional and integration requirements of Distributed Energy Resources (DER,) are detailed. Smart Consumption Infrastructure elements of Distribution Management Systems (DMS,) Automated Metering Infrastructure (AMI,) Smart Homes (SH), and Smart Appliances (SA,) are discussed. Following the introductory section, this paper summarizes contributions included in the double issue 42(3–4) of the Electric Power Components and Systems Journal. To begin, papers are offered discussing smart grid activities in China, India, and the development of a Smart Grid roadmap for the US State of Kentucky. The approaches of each of these cases reflect the diversity of policy initiatives in these jurisdictions. Two state of the art reviews are given next. The first considers distribution network active management and future development trends in technologies and methods, where centralized and decentralized management frameworks and applying agent-based coordination are discussed. The second offers a review of smart home technologies and the goals of an energy management system (SHEMS). This section is concluded by a letter providing an overview of recent and expected advances nanotechnology applications in Smart Grid. Following the state of the art review section, ten papers offering new and innovative research approaches and results are included. These papers cover Smart Grid topics such as real-time energy control approach for smart home energy management systems, optimal operation of energy- efficient buildings with Combined Heat and Power (CHP) systems, energy management and control of Electric Vehicle (EV) charging stations, voltage-frequency control of a voltage source inverter (VSI) in a smart islanded microgrid, smart generation scheduling for wind-thermal-pumped storage systems, optimized power system restoration, robust data transmission upon compressive sensing, data fusion for wide-area oscillation monitoring, satellite based GPS synchronized monitoring systems, stability in Smart Grid with emerging renewable energy techno- logies.

Smart Home Activities: A Literature Review

The increasing interest in smart home technologies has created a need for a comprehensive literature survey. This article reviews the goals of a smart home energy management system, along with related definitions, applications, and information about the manufacturing of its components. The challenges associated with smart home energy management systems and possible solutions are examined, and the energy factors that contribute to a customer's electricity bill are discussed. A number of price schemes and the load models needed for solving related scheduling optimization problems are also presented, including a review of the literature related to energy management system scheduling with respect to its control, automation, and communication.

Real-time Energy Control Approach for Smart Home Energy Management System

This article investigates a real-time energy control approach for a home energy management system, including the electric water heater, air conditioner, clothes dryer, electric vehicle, photovoltaic cell, critical loads, and battery system. A demand response mechanism is proposed to enable households to participate in demand response services. Half-hour-ahead rolling optimization and a real-time control strategy are combined to achieve household economic benefits and ability to deal with complex operating environments. A fuzzy logic controller is utilized to determine battery charging/ discharging power; proper rules are proposed to ensure benefits from operating the battery under the real-time electricity price. The simulation test results indicate that the proposed control approach can optimize the schedule for home appliances and battery charging/ discharging behavior, even if the forecasted information is not accurate. A physical test platform has also been established and tested in the lab to support the operation of the whole system.

Wireless Sensor Networking Architecture of Polytropon: An Open Source Scalable Platform for the Smart Grid

This article presents the overall design methodology followed for an open source Wireless Sensor Networking platform that aims at the remote energy monitoring of home appliances in real-time using web technologies. This platform can be a paradigm for building open source devices for smart metering and home energy management by controlling electric loads, thus contributing to the development of the smart grid infrastructure. The Polytropon platform is designed as a research tool for experimenting with the various hardware and software components that can be integrated in the nodes, by following the open source hardware and software guidelines for development.

Smart Home Energy Management with Electric Vehicles Considering Battery Degradation

With the development of vehicles-to-grid (V2G) technology, electric vehicles (EVs) are receiving increasing attention in recent years. This paper describes a smart home energy dispatch model including EVs and flexible appliances. In this model, the home's electricity cost is minimized through optimal dispatch, while the comfort level of home is considered. A simplified method to measure battery degradation is proposed. The model is solved by the dissipative particle swarm optimization (DPSO) algorithm. Simulation results show that the battery lifetime can be extended when the battery degradation is considered in the charging and discharging of the EV. With the decrease in the battery cost, the user has more incentives to use the EV as a storage device to reduce the electricity cost.

Hardware Design of Smart Home Energy Management System With Dynamic Price Response

The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.

임베디드 전력 모니터링 보안 모듈 설계

스마트 그리드용 전력망이 구축시범사업이 진행됨에 따라 스마트 디지털가전 AV기술, 냉난방 습도 공기 자동관리를 위한 복합에너지 관리기능을 담당하는 스마트 홈 에너지관리기술, 노약자와 장애인을 위한 주거설계와 가족 구성원에 대한 개인별 바이오정보 측정을 담당하게 될 헬스케어 기술, 생체인식 보안과 동작감지센서 등을 다루는 스마트 홈 시큐리티 기술 등 연구가 진행되고 있다. 본 논문에서는 물리적 공격에 취약한 외부에 노출되는 문제점이 발생하게 되는 스마트미터기에 대응되는 암호기술을 분석하고 단말기의 효율성을 극대화 할수 있는 스마트 미터기 단말기용 보안시스템 설계를 제안하였다. The demonstration project of the electrical grid for Smart grid is progressed, the smart digital appliances AV technology, Smart home energy management technology charging the management function of complex energy for the automation management of air conditioning and heating, humidity and air, the health care technology charging the design of housing for the elderly and disabled and the measurement of individual bio information, and the Smart home security technology dealing with the biometric security and motion sensors, etc. have been studied. The power monitoring terminal which uses a variety of wired and wireless networks and protocol is the target additionally to be considered in addition to the security vulnerabilities that was occurred in the existing terminal. In this research paper, the author analyzes the cryptographic techniques corresponding to the smart meter occurred by the problems that are exposed on the outside which are vulnerable to physical attacks, and intends to propose the design of the security systems for the Smart meter terminal being able to maximize the efficiency of the terminal.

Hybrid soft computing algorithmic framework for smart home energy management

Energy management in Smart Home environments is undoubtedly one of the pressing issues in the Smart Grid research field. The aim typically consists in developing a suitable engineering solution able to maximally exploit the availability of renewable resources. Due to the presence of diverse cooperating devices, a complex model, involving the characterization of nonlinear phenomena, is indeed required on purpose. In this paper an Hybrid Soft Computing algorithmic framework, where genetic, neural networks and deterministic optimization algorithms jointly operate, is proposed to perform an efficient scheduling of the electrical tasks and of the activity of energy resources, by adequately handling the inherent nonlinear aspects of the energy management model. In particular, in order to address the end-user comfort constraints, the home thermal characterization is needed: this is accomplished by a nonlinear model relating the energy demand with the required temperature profile. A genetic algorithm, based on such model, is then used to optimally allocate the energy request to match the user thermal constraints, and therefore to allow the mixed-integer deterministic optimization algorithm to determine the remaining energy management actions. From this perspective, the ability to schedule the tasks and allocate the overall energy resources over a finite time horizon is assessed by means of diverse computer simulations in realistic conditions, allowing the authors to positively conclude about the effectiveness of the proposed approach. The degree of realism of the simulated scenario is confirmed by the usage of solar energy production forecasted data, obtained by means of a neural-network based algorithm which completes the framework.

Energy Information Communication Technologies for Smart Home Applications

This paper reviews previous and recent trends in energy information communication technologies (EICT) for smart home applications. Relevant EICT publications on smart homes are reviewed. Smart home and smart home energy management system (SHEMS) related concepts are described, followed by a thorough review of SHEMS and EICT technologies. As is increasingly recognized, EICT is a highly effective means of monitoring, controlling, and conserving energy consumption in smart home applications. Additionally, various EICT approaches are surveyed to evaluate the feasibility of smart home applications by discussing historical developments and introducing advanced EICT methods. Importantly, in addition to surveying the latest trends, this study contributes to efforts to further advanced EICT applications in smart homes.

Observe, Learn, and Adapt (OLA)—An Algorithm for Energy Management in Smart Homes Using Wireless Sensors and Artificial Intelligence

The need for energy efficient and intelligent systemic solutions, has lead many researchers around the world to investigate and evaluate the existing technologies in order to create solutions that would be adopted for the near future intelligent homes and buildings, aiding in smart grid initiatives. In this paper an algorithm based on the adaptable learning system principles is presented. The proposed algorithm utilizes the adaptable learning system concepts. The Observe, Learn, and Adapt (OLA) algorithm proposed is the result of integration of wireless sensors and artificial intelligence concepts towards the same objective: adding more intelligence to a programmable communicating thermostat (PCT), for better energy management and conservation in smart homes. A house simulator was developed and used as an “expert system shell” to assist in implementation and verification of the OLA algorithm. The role of PCT is to provide consumer with a means to manage and reduce energy use, while accommodating their every day schedules, preferences and needs. In this paper, the actual results of learning and adaptability of a PCT equipped with OLA, as a result of the occupant's pattern/schedule changes, and in general, the overall system improvements with respect to energy consumption and savings are demonstrated via simulation for the zone controlled home equipped with OLA and Knowledge Base, versus a home without zone control, Knowledge Base nor OLA.

Optimization of Power Scheduling for Energy Management in Smart Homes

Considerable study has been made to improve the efficacy of appliances for energy management in smart homes. Many power scheduling methods for energy management in smart homes have been done, and there exists numerous actual examples. This paper reviews different Journal and conference papers covering a broad range of energy optimization methods for smart home applications. The concept of smart home is first introduced; then, various methods are analyzed, including fuzzy logic, neural networks, heuristic methods, and evolutionary algorithms

A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation

With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.

Smart home energy management system using IEEE 802.15.4 and zigbee

Wireless personal area network and wireless sensor networks are rapidly gaining popularity, and the IEEE 802.15 Wireless Personal Area Working Group has defined no less than different standards so as to cater to the requirements of different applications. The ubiquitous home network has gained widespread attentions due to its seamless integration into everyday life. This innovative system transparently unifies various home appliances, smart sensors and energy technologies. The smart energy market requires two types of ZigBee networks for device control and energy management. Today, organizations use IEEE 802.15.4 and ZigBee to effectively deliver solutions for a variety of areas including consumer electronic device control, energy management and efficiency, home and commercial building automation as well as industrial plant management. We present the design of a multi-sensing, heating and airconditioning system and actuation application - the home users: a sensor network-based smart light control system for smart home and energy control production. This paper designs smart home device descriptions and standard practices for demand response and load management Smart Energy applications needed in a smart energy based residential or light commercial environment. The control application domains included in this initial version are sensing device control, pricing and demand response and load control applications. This paper introduces smart home interfaces and device definitions to allow interoperability among ZigBee devices produced by various manufacturers of electrical equipment, meters, and smart energy enabling products. We introduced the proposed home energy control systems design that provides intelligent services for users and we demonstrate its implementation using a real testbad.