Non-Intrusive Load Monitoring Assessment: Literature Review and Laboratory Protocol

To evaluate the accuracy of NILM technologies, a literature review was conducted to identify any test protocols or standardized testing approaches currently in use. The literature review indicated that no consistent conventions were currently in place for measuring the accuracy of these technologies. Consequently, PNNL developed a testing protocol and metrics to provide the basis for quantifying and analyzing the accuracy of commercially available NILM technologies. This report discusses the results of the literature review and the proposed test protocol and metrics in more detail

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

Intrusive and Non-Intrusive Load Monitoring (A Survey)

There is not discussion about the need of energy conservation, it is well known that energy resources are limited moreover the global energy demands will double by the end of 2030, which certainly will bring implications on the environment and hence to all of us. Non-Intrusive load monitoring (NILM) is the process of recognize electrical devices and its energy consumption based on whole home electric signals, where this aggregated load data is acquired from a single point of measurement outside the household. The aim of this approach is to get optimal energy consumption and avoid energy wastage. Intrusive load monitoring (ILM) is the process of identify and locate single devices through the use of sensing systems to support control, monitor and intervention of such devices. The aim of this approach is to offer a base for the development of important applications for remote and automatic intervention of energy consumption inside buildings and homes as well.  Appliance discerns can be tackled using approaches from data mining and machine learning, finding out the techniques that fit the best this requirements, is a key factor for achieving feasible and suitable appliance load monitoring solutions. This paper presents common and interesting methods used. Privacy concerns have been one of the bigger obstacles for implementing a widespread adoption of these solutions. The implementation of security over these approaches along with fine-grained energy monitoring would lead to a better public agreement of these solutions and hence a faster adoption of such approaches. This paper reveals a lack of security over these approaches with a real scenario. &nbsp

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

Benefits and challenges of using smart meters for advancing residential water demand modeling and management: a review

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world

Energy-Use Feedback Engineering - Technology and Information Design for Residential Users

The research presented in this study covers a first design iteration of energy feedback for residential users. This research contributes with a framework and new insights into the study of energy-use information for residential users, which exemplifies the challenges and potential of integrating information technology in this part of the energy system

A Dynamical Systems Approach to Energy Disaggregation

Energy disaggregation, also known as non-intrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances. Studies have shown that simply providing disaggregated data to the consumer improves energy consumption behavior. However, placing individual sensors on every device in a home is not presently a practical solution. Disaggregation provides a feasible method for providing energy usage behavior data to the consumer which utilizes currently existing infrastructure. In this paper, we present a novel framework to perform the energy disaggregation task. We model each individual device as a single-input, single-output system, where the output is the power consumed by the device and the input is the device usage. In this framework, the task of disaggregation translates into finding inputs for each device that generates our observed power consumption. We describe an implementation of this framework, and show its results on simulated data as well as data from a small-scale experiment.Comment: Submitted to 52nd IEEE Conference on Decision and Control (CDC 2013

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