NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Acoustic and Device Feature Fusion for Load Recognition

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multilayer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. The highest recognition performance however is shown by support vector machines, for the device and audio recognition experiments. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only

Designing Artificial Neural Networks (ANNs) for Electrical Appliance Classification in Smart Energy Distribution Systems

En este proyecto se abordará el problema de la desagregación del consumo eléctrico a través del diseño de sistemas inteligentes, basados en redes neuronales profundas, que puedan formar parte de sistemas más amplios de gestión y distribución de energía. Durante la definición estará presente la búsqueda de una complejidad computacional adecuada que permita una implementación posterior de bajo costo. En concreto, estos sistemas realizarán el proceso de clasificación a partir de los cambios en la corriente eléctrica provocados por los distintos electrodomésticos. Para la evaluación y comparación de las diferentes propuestas se hará uso de la base de datos BLUED.This project will address the energy consumption disaggregation problem through the design of intelligent systems, based on deep artificial neural networks, which would be part of broader energy management and distribution systems. The search for adequate computational complexity that will allow a subsequent implementation of low cost will be present during algorithm definition. Specifically, these systems will carry out the classification process based on the changes caused by the different appliances in the electric current. For the evaluation and comparison of the different proposals, the BLUED database will be used.Máster Universitario en Ingeniería Industrial (M141

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

Residential Energy Management for Renewable Energy Systems Incorporating Data-Driven Unravelling of User Behavior

The penetration of distributed energy resources (DERs) such as photovoltaic (PV) at the residential level has increased rapidly over the past year. It will inevitably induce a paradigm shift in end-user and operations of local energy markets. The energy community with high integration of DERs initiative allows its users to manage their generation (for prosumers) and consumption more efficiently, resulting in various economic, social, and environmental benefits. Specifically, the local energy communities and their members can legally engage in energy generation, distribution, supply, consumption, storage, and sharing to increase levels of autonomy from the power grid, advance energy efficiency, reduce energy costs, and decrease carbon emissions. Reducing energy consumption costs is difficult for residential energy management without understanding the users' preferences. The advanced measurement and communication technologies provide opportunities for individual consumers/prosumers and local energy communities to adopt a more active role in renewable-rich smart grids. Non-intrusive load monitoring (NILM) monitors the load activities from a single point source, such as a smart meter, based on the assumption that different appliances have different power consumption levels and features. NILM can extract the users' load consumption from the smart meter to support the development of the smart grid for better energy management and demand response (DR). Yet to date, how to design residential energy management, including home energy management systems (HEMS) and community energy management systems (CEMS), with an understanding of user preferences and willingness to participate in energy management, is still far from being fully investigated. This thesis aims to develop methodologies for a resident energy management system for renewable energy systems (RES) incorporating data-driven unravelling of the user's energy consumption behaviour

Conserving Energy with No Watt Left Behind

Facilities managers for industrial and commercial sites want to develop detailed electrical consumption profiles of their electrical and electromechanical loads, including expensive physical plant for heating, ventilation, and air conditioning (HVAC) and equipment for manufacturing and production. This information is essential in order to understand and optimize energy consumption, to detect and solve equipment failures and problems, and to facilitate predictive maintenance of electromechanical loads. As energy costs rise, residential customers are also developing a growing interest in understanding the magnitude and impact of their electrical consumption quickly, easily, and informatively

Smart grids: smart meters and non intrusive load monitoring

El objetivo de este proyecto consiste en sintetizar los conceptos generales de las redes inteligentes (Smart Grids), los cambios que se prevén en la red eléctrica y las principales tecnologías que apoyaran el desarrollo de las mismas. Una Smart Grid es una sistema que permite la comunicación bidireccional entre el consumidor final y las compañías eléctricas, de forma que la información proporcionada por los consumidores pueda ser utilizada por las compañías eléctricas para permitir una operación mas eficiente de las red eléctrica, así como ofrecer nuevos servicios a los clientes. El desarrollo de las Smart Grids es esencial si la comunidad global quiere alcanzar objetivos comunes de seguridad energética, desarrollo económico y mitigación del cambio climático. Para ello, se están desarrollando e implementando nuevas tecnologías como los medidores inteligentes (Smart Meters) y nuevas técnicas de medida de consumo eléctrico como la monitorización no intrusiva (Non Intrusive Load Monitoring). Los Smart Meters son medidores de electricidad, agua o gas que recopilan de forma automática los datos de medida y los envían a las compañías eléctricas permitiendo a estas tener una mejor visión de la distribución eléctrica y proporcionan a sus clientes un mayor conocimiento de su propio consumo. La monitorización no intrusiva es una técnica que detecta los eventos de aparatos eléctricos analizando la demanda total de la carga. Esto es posible debido a que los aparatos presentan características especiales en los momentos de conexión y desconexión consistentes en cambios tanto positivos como negativos en las potencias activa y reactiva. Como dichas características son únicas en cada dispositivo, es posible reconocer el perfil de cada uno de ellos pudiendo saber que dispositivos se están encendiendo o apagando, así como el consumo eléctrico de cada uno de ellos. Esto es lo que ofrece la tecnología Plugwise, que mediante el uso de sus dispositivos permite monitorizar y controlar el consumo eléctrico de una vivienda, oficina o empresa y poder ver los resultados en nuestro propio Smartphone o PC. El uso de tecnología Plugwise en combinación con un Smart Meter permite que tanto clientes como compañías eléctricas sean conscientes de cuanto, como y donde se consume la electricidad