Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

NON-INTRUSIVE LOAD MONITORING: IMPLEMENTATION EFFECTS AND DISTRIBUTION PROSPECTS

The digital transition in the electric power industry is a promising goal for the development of the industry. In recent years, a wide range of technologies has been introduced into various types of activities of energy companies, including significant attention being paid to technologies that implement demand-side management, transferring consumers from the passive category to active consumers, and also opening up new opportunities in energy management. Non-intrusive load monitoring technology is of significant interest for both electricity suppliers and consumers in the USA and EEC countries, however, a Russian-language study is being carried out for the first time.The purpose of the study is to consider the concept of non-intrusive load monitoring, to formulate and systematize the effects for electric power industry entities and consumers of electricity from the introduction of technology.A review of literary sources is carried out, the most cited articles on this topic are analyzed. To calculate the propagation rate of non-intrusive load monitoring, the Bass innovation diffusion model was used. The model allows to perform an assessment based on data on similar products and has established itself as sufficiently effective for predicting the distribution of durable goods, the actual information for which has not yet been collected.For the first time, a classification of effects arising from the introduction of technology is proposed. The paper obtained a range of effects for households, energy companies, business and government. The calculation of the technology distribution rate showed that without the use of technological corridors and the systematic introduction by energy companies, the peak of adoption can be reached by 8 years from the start of implementation.Non-intrusive load monitoring allows you to get a wide range of data in order to further optimize energy consumption, increase the efficiency of enterprises, monitor the operation of equipment. There are great opportunities in the commercialization of collected data

НЕИНТРУЗИВНЫЙ МОНИТОРИНГ НАГРУЗКИ: ЭФФЕКТЫ ВНЕДРЕНИЯ И ПЕРСПЕКТИВЫ РАСПРОСТРАНЕНИЯ

The digital transition in the electric power industry is a promising goal for the development of the industry. In recent years, a wide range of technologies has been introduced into various types of activities of energy companies, including significant attention being paid to technologies that implement demand-side management, transferring consumers from the passive category to active consumers, and also opening up new opportunities in energy management. Non-intrusive load monitoring technology is of significant interest for both electricity suppliers and consumers in the USA and EEC countries, however, a Russian-language study is being carried out for the first time.The purpose of the study is to consider the concept of non-intrusive load monitoring, to formulate and systematize the effects for electric power industry entities and consumers of electricity from the introduction of technology.A review of literary sources is carried out, the most cited articles on this topic are analyzed. To calculate the propagation rate of non-intrusive load monitoring, the Bass innovation diffusion model was used. The model allows to perform an assessment based on data on similar products and has established itself as sufficiently effective for predicting the distribution of durable goods, the actual information for which has not yet been collected.For the first time, a classification of effects arising from the introduction of technology is proposed. The paper obtained a range of effects for households, energy companies, business and government. The calculation of the technology distribution rate showed that without the use of technological corridors and the systematic introduction by energy companies, the peak of adoption can be reached by 8 years from the start of implementation.Non-intrusive load monitoring allows you to get a wide range of data in order to further optimize energy consumption, increase the efficiency of enterprises, monitor the operation of equipment. There are great opportunities in the commercialization of collected data.Цифровой переход в электроэнергетике является перспективной целью развития отрасли. В последние годы широкий спектр технологий внедряется в различные виды деятельности энергокомпаний, в том числе значимое внимание уделяется технологиям, реализующим управление спросом на электроэнергию, переводящим потребителей из категории пассивных потребителей в активные, а также открывающим новые возможности в энергоменеджменте. Технология неинтрузивного мониторинга нагрузки представляет существенный интерес как для поставщиков электроэнергии, так и для потребителей в США и странах ЕЭС, однако исследование в РФ выполняется впервые.Цель настоящего исследования – рассмотреть понятие неинтрузивного мониторинга нагрузки, сформулировать и систематизировать эффекты для субъектов электроэнергетики и потребителей электроэнергии от внедрения технологии.Выполнен обзор литературных источников, проанализированы наиболее цитируемые статьи по данной тематике. Для расчета темпа распространения неинтрузивного мониторинга нагрузки была использована модель диффузии инноваций Басса. Модель позволяет выполнить оценку на основе данных о продуктах-аналогах и зарекомендовала себя как достаточно эффективная для прогнозирования распространения товаров длительного пользования, фактическая информация по которым еще не собрана.Впервые предложена классификация эффектов, возникающих при внедрении технологии. В работе получен спектр эффектов для домохозяйств, энергетических компаний, бизнеса и органов власти. Расчет темпов распространения технологии показал, что без использования технологических коридоров и систематического внедрения неинтрузивного мониторинга нагрузки энергокомпаниями пик принятия может быть достигнут на восьмой год от старта реализации.Неинтрузивный мониторинг нагрузки позволяет получить широкий диапазон данных с целью дальнейшей оптимизации энергопотребления, повышения эффективности деятельности предприятий, осуществления контроля за работой оборудования. Перед энергокомпаниями открываются новые возможности в коммерциализации собранных данных

Gridchain: an investigation of privacy for the future local distribution grid

As part of building the smart grid, there is a massive deployment of so-called smart meters that aggregate information and communicate with the back-end office, apart from measuring properties of the local network. Detailed measurements and communication of, e.g., consumption allows for remote billing, but also in finding problems in the distribution of power and overall to provide data to be used to plan future upgrades of the network. From a security perspective, a massive deployment of such Internet of Things (IoT) components increases the risk that some may be compromised or that collected data are used for privacy-sensitive inference of the consumption of households. In this paper, we investigate the privacy concerns regarding detailed readings of smart meters for billing purposes. We present Gridchain, a solution where households can opt-in to hide their consumption patterns and thus make Non-Intrusive Load Monitoring (NILM) more challenging. Households form groups where they can trade real consumption among themselves to achieve reported consumption that would be resistant to NILM. Gridchain is built on a publish/subscribe model and uses a permissioned blockchain to record any trades, meaning that dishonest households can be discovered and punished if they steal from other households in the group or the electricity company in the end. We implement and release a proof of concept of Gridchain and use public datasets to allow reproducibility. Our results show that even if an attacker has access to the reported electricity consumption of any member of a Gridchain group, this reported consumption is significantly far from the actual consumption to allow for a detailed fingerprint of the household activities

Non-intrusive load management system for residential loads using artificial neural network based arduino microcontroller

The energy monitoring is one of the most important aspects of energy management. In fact there is a need to monitor the power consumption of a building or premises before planning technical actions to minimize the energy consumption. In traditional load monitoring method, a sensor or a group of sensors attached to every load of interest to monitor the system, which makes the system costly and complex. On the other hand, by Non-Intrusive Load Monitoring (NILM) the aggregated measurement of the building’s appliances can be used to identify and/or disaggregate the connected appliances in the building. Therefore, the method provides a simple, reliable and cost effective monitoring since it uses only one set of measuring sensors at the service entry. This thesis aims at finding a solution in the residential electrical energy management through the development of Artificial Neural Network Arduino (ANN-Arduino) NILM system for monitoring and controlling the energy consumption of the home appliances. The major goal of this research work is the development of a simplified ANN-based non-intrusive residential appliances identifier. It is a real-time ANN-Arduino NILM system for residential energy management with its performance evaluation and the calibration of the ZMPT101B voltage sensor module for accurate measurement, by using polynomial regression method. Using the sensor algorithm obtained, an error of 0.9% in the root mean square (rms) measurement of the voltage is obtained using peak-peak measurement method, in comparison to 2.5% when using instantaneous measurement method. Secondly, a residential energy consumption measurement and control system is developed using Arduino microcontroller, which accurately control the home appliances within the threshold power consumption level. The energy consumption measurement prototype has an accurate power and current measurement with error of 3.88% in current measurement when compared with the standard Fluke meter. An ANN-Arduino NILM system is also developed using steady-state signatures, which uses the feedforward ANN to identify the loads when it received the aggregated real power, rms current and power factor from the Arduino. Finally, the ANN-Arduino NILM based appliances’ management and control system is developed for keeping track of the appliances and managing their energy usage. The system accurately recognizes all the load combinations and the load controlling works within 2% time error. The overall system resulted into a new home appliances’ energy management system based on ANN-Arduino NILM that can be applied into smart electricity system at a reduced cost, reduced complexity and non-intrusively

The 1991 Marshall Space Flight Center research and technology

A compilation of 194 articles addressing research and technology activities at the Marshall Space Flight Center (MSFC) is given. Activities are divided into three major areas: advanced studies addressing transportation systems, space systems, and space science activities conducted primarily in the Program Development Directorate; research tasks carried out in the Space Science Laboratory; and technology programs hosted by a wide array of organizations at the Center. The theme for this year's report is 'Building for the Future'

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal