Big data analytics for smart home energy management system based on IOMT using AHP and WASPAS

Smart Home Energy Management (SHEM) systems allow to optimize the usage of resources in our houses while making them comfortable to humans. However, in the development of SHEM systems only limited attention has been put to the impact of the system performance on the Quality of Experience (QoE), considering the humans mostly as the final recipient of the service rather than the central component of the whole service. In this paper, we consider a SHEM system that, on the basis of pre-created user profiles, aims to reduce the electricity costs while preserving the QoE perceived by the user (evaluated in terms of perceived user annoyance due to the shift of the appliance's starting time). We focus on the identification of the user profile during the live sessions in the SHEM scenario and on the analysis of the impact of such task in the SHEM system performance. To evaluate the impact of profile selection, we conducted experiments in which 12 people were asked to provide their feedback each time the SHEM system proposed a modified starting time for an appliance with respect to the user's preferences. From experiments results it is found that the SHEM system needs on average just one feedback from the user to find the best user profile.

In this proposal, we describe a Smart Home Energy Management System (SHEMS) for effective home power management. This system, after gathering information about the various home appliances, monitors and controls their performance for the most efficient power consumption scenario. To aid this function, we also describe a PLC Power Controlled Outlet Module (PPCOM). A home server connected to the PPCOM network is proposed as part of a Star topology. The server is provided with required parameters of all devices on the home grid by microprocessors of each device, enabling it to reach decisions about their performance. Elementary tests & surveys indicate that our design can be implemented practically and does not require any radical changes in the existing infrastructure.

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.

Chapter 3 - Smart home energy management system: concept, architecture, infrastructure, challenges, and energy management

In this paper, a Quality of Experience (QoE)-aware Smart Home Energy Management (SHEM) system is proposed. Firstly, a survey has been conducted on 64 people to investigate the degree of satisfaction perceived when the starting time of appliances was postponed or anticipated with respect to the preferred time. Secondly, the results were clustered in different profiles using the k-means algorithm to control appliances' working time according to the detected user profile. Thirdly, a SHEM system is run that relies on two algorithms: the QoE-aware Cost Saving Appliance Scheduling (Q-CSAS) and the QoE-aware Renewable Source Power Allocation (Q-RSPA). The former is aimed at scheduling controllable loads based on users' profile preferences and Time-of-Use (TOU) electricity prices, thus taking into account the level of annoyance perceived when a task is postponed or anticipated. The latter re-allocates the starting time of appliances whenever a surplus of energy has been made available by Renewable Energy Sources (RES). This re-allocation takes place using a distributed max-consensus negotiation algorithm. The objective is that of scheduling the appliances starting time so that a trade-off between cost saving and annoyance perceived is achieved. As demonstrated by simulation results, the two algorithms ensure a cost saving that goes from 19% to 84% depending on the presence of RES, with a resulting average annoyance factor value of 1.01 to 1.03.

An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages

With the fast development of Wireless Sensor Networks (WSNs) and RFID technology, many Internet of Things (IOT) applications have been deployed in recent years. Smart home energy management systems form part of the smart grid program and are a fast developing smart home application area. Inadequate security is a big issue in smart home energy management systems. Most security protocols widely used for computer network and internet security cannot be implemented in smart home energy management systems as they are computational expensive for the wireless sensor nodes used in smart home applications. The major issue in the security of smart home energy management systems is the establishment of the initial session key between the wireless nodes and control center. In this paper, we propose a lightweight key establishment protocol for smart home energy management systems and present the implementation details of the protocol.

Smart Home Energy Management Systems in Internet of Things networks for green cities demands and services

This paper presents a laboratory hardware system, developed in the Department of Electrical Power Systems of University Politehnica of Bucharest, that simulate an energy management system to be applied in a smart home. The core of the system is a controller that is capable of switching on/off various domestic appliances as a response to price signals. The system may be capable of communicating with all loads and with the main meter, and may provide information about the power quality. Also, the system may be capable of responding to supplier's signals in order to provide a demand response service. According to Siemens (5), the buildings are responsible for 40% of the world energy consumption and for 21% of the total greenhouse emissions. For these reasons, buildings are key elements in the targets to reduce the energy consumption and to implement sustainable development programs. Implementation of advanced technologies and transforming the buildings into manageable entities may help reducing the greenhouse emissions by up to 40%. The smart home concept, together with the energy management systems for small applications, are normal evolutions in the implementation process of the smart grids concept towards transforming the traditional consumers in more active ones, becoming in some cases prosumers. Various solutions have been proposed in the literature, and innovative projects have been implemented in pilot projects, many of them focusing on metering and data management. A connected home platform and development framework for design, development and deployment of smart home services is presented in (2), whereas a lightweight key establishment protocol for smart home energy management systems and the implementation details of the protocol are proposed in (3). One challenging technical issues is the compatibility between equipments. The Zigbee technology for application in the smart home is presented in (4), where a new routing protocol DMPR (Disjoint Multi Path based Routing) to improve the performance of the ZigBee sensor networks is proposed. The interaction between the user and the home energy management system is decisive in helping the customer to easily adopt the new technology. A user interaction interface for energy management in smart homes is proposed in (5). Various control and optimization algorithms have been proposed. An optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in the presence of a real-time pricing tariff combined with inclining block rates is proposed in (6). Authors of (7) and (8) propose optimization algorithms to be implemented in the home energy management systems to determine the optimal operation of residential appliances within 5-minute time slots while considering uncertainties in real-time electricity prices.

In the future, automated smart home energy management systems (SHEMSs) will assist residential energy users to schedule and coordinate their energy use. In order to undertake efficient and robust scheduling of distributed energy resources, such a SHEMS needs to consider the stochastic nature of the household's energy use and the intermittent nature of its distributed generation. Currently, stochastic mixed-integer linear programming (MILP), particle swarm optimization and dynamic programming approaches have been proposed for incorporating these stochastic variables. However, these approaches result in a SHEMS with very costly computational requirements or lower quality solutions. Given this context, this paper discusses the drawbacks associated with these existing methods by comparing a SHEMS using stochastic MILP with heuristic scenario reduction techniques to one using a dynamic programming approach. Then, drawing on analysis of the two methods above, this paper discusses ways of reducing the computational burden of the stochastic optimization framework by using approximate dynamic programming to implement a SHEMS.

The increasing acceptance of electric vehicles (EVs) and renewable energy sources (RES) has led to the need for a unified energy management framework that can integrate multiple energy systems, including Vehicle-to-Home (V2H) units, Smart Home Energy Management Systems (SHEMS), Solar PV systems, and the grid. The framework uses a centralized control strategy that makes decisions based on real-time integration of a Vehicle-to-Home (V2H) unit with a Smart Home Energy Management System (SHEMS), which can provide a solution to these challenges by enabling bidirectional power flow between the EV battery and the home. We proposed a unified energy management framework that integrates a Ve- hicle-to-Home (V2H) unit with a SHEMS using a Perturb and Observe (P-&-O) algorithm. The proposed framework allows for the optimization of energy usage in the home by utilizing the battery of an EV, which can be charged during off-peak hours and discharged during peak hours. The P&O algorithm is a well-known algorithm used in photovoltaic systems to track the Maximum Power Point (MPP) of a solar panel. The algorithm is also applicable to EV charging and discharging systems. The algorithm uses a per- turbation signal to change the charging rate of the EV battery and observes the resulting change in the bat- tery voltage. The simulation results can also provide benefits to EV owners, such as reduced electricity costs and increased flexibility in managing their EV charging, helping to achieve efficient and sustainable energy management in residential areas. Keywords: Solar panel, Electric Vehicle Battery, Vehicle-to-Home (V2H), Smart Home Energy Manage- ment Systems (SHEMS), Demand side management.

The global increasing demand for energy has brought attention to the need for energy efficiency. Markedly noticeable in developing areas, energy challenges can be at-tributed to the losses in the distribution and transmission sys-tems, and insufficient demand-side energy management. Demand-oriented systems have been widely proposed as feasible solutions. Smart Home Energy Management Systems have been proposed to include smart Internet of Things (IoT)-capable devices in an ecosystem programmed to achieve energy efficiency. However, these systems apply only to already-smart devices and are not appropriate for the many locales where a majority of appliances are not yet IoT-capable. In this paper, we establish the need to pay attention to non-smart appliances, and propose a solution for incorporating such devices into the energy-efficient IoT space. As a solution, we propose Homergy, a smart IoT-based Home Energy Management Solution that is useful for any market –advanced and developing. Homergy consists of the Homergy Box (which is an IoT device with Internet connectivity, an in-built microcontroller and opto-coupled relays), a NoSQL cloud-based database with streaming capabilities, and a secure cross-platform mobile app (Homergy Mobile App). To validate and illustrate the effectiveness of Homergy, the system was deployed and tested in 3 different consumer scenarios: a low-consuming house, a single-user office and a high-consuming house. The results indicated that Homergy produced weekly energy savings of 0.5 kWh for the low-consuming house, 0.35 kWh for the single-user office, and a 13-kWh improvement over existing smart-devices-only systems in the high-consuming house.

Smart home energy management processes support through machine learning algorithms

Today, organizations use IEEE802.15.4 and 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. The Smart home energy network has gained widespread attentions due to its flexible integration into everyday life. This next generation green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. The green home energy network gradually forms a complex system to process various tasks. Developing this trend, we suggest a new Smart Home Energy Management System (SHEMS) based on an IEEE802.15.4 and (we call it as a ZigBee sensor network). The proposed smart home energy management system divides and assigns various home network tasks to appropriate components. It can integrate diversified physical sensing information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. We develop a new routing protocol DMPR (Disjoint Multi Path based Routing) to improve the performance of our sensor networks. This paper introduces the proposed home energy control system's design that provides intelligent services for users. We demonstrate its implementation using a real environment.

Internet of Things, it is also called as Internet of Objects because it may be a network between different objects. IoT is employed within the universe to speak between the physical objects. In recent days implementation of home automation became a prominent area of research. This paper presents smart home energy management system with remote monitoring and cargo balancing. Smart home energy management system is employed to manage and monitor the house appliances remotely or locally. Sensors are employed in the smart home system to manage the house appliances. Smart home system using IoT take our day to day life into a comfort and convenient area. Home security and protection is incredibly important. Smart home energy management system is additionally used for security purpose. Security of home is managed by sending notifications to the user using internet just in case of any trespasser and it may also ring alarm. Smart home allows for prompt accessibility, efficient usage of electricity and supply user convenience. Home automation reduces unnecessary human efforts and improves the quality of living of the people within the society. It has been explained that it utilizes the combination of cloud networking and wireless communication, to provide the user with devices within their home like lights, fans, and appliances and to store the data in the cloud. The system will change automatically on the basis of the sensors data. This method is intended to be low-cost and expandable, enabling control for a wide range of devices.