Building appliances energy performance assessment

Trabalho de Projeto de Mestrado, Informática, 2021, Universidade de Lisboa, Faculdade de CiênciasO consumo de energia tem vindo a crescer na União Europeia todos os anos, sendo de prever que, a curto prazo, se torne insustentável. No sentido de prevenir este cenário, a Comissão Europeia decidiu definir uma Estratégia Energética para a União Europeia, destacando dois objetivos: aumentar a eficiência energética e promover a descarbonização. Atualmente, cerca de 72% dos edifícios existentes na União Europeia não são energeticamente eficientes. Este problema motivou-nos à pesquisa e criação de soluções que permitam uma melhor avaliação do consumo energético por dispositivos elétricos em edifícios residenciais. Neste contexto, o trabalho desenvolvido nesta tese consiste no desenho de uma solução de monitorização remota que recolhe informações de consumo energético recorrendo a técnicas de intrusive load monitoring, onde cada dispositivo elétrico individual é continuamente monitorizado quanto ao seu consumo energético. Esta abordagem permite compreender o consumo de energia, em tempo real e no dia-a-dia. Este conhecimento oferece-nos a capacidade de avaliar as diferenças existentes entre as medições laboratoriais (abordagem utilizada no sistema de rotulagem de equipamentos elétricos de acordo com a sua eficiência energética) e os consumos domésticos estimados. Para tal, nesta tese exploram-se abordagens de machine learning que pretendem descrever padrões de consumo, bem como reconhecer marcas, modelos e que funções os dispositivos elétricos estarão a executar. O principal objetivo deste trabalho é desenhar e implementar um protótipo de uma solução de IoT flexível e de baixo custo para avaliar equipamentos elétricos. Será utilizado um conjunto de sensores que recolherá dados relacionados com o consumo de energia e os entrega à plataforma SATO para serem posteriormente processados. O sistema será usado para monitorar aparelhos comumente encontrados em residências. Além disso, o sistema terá a capacidade de monitorizar o consumo de água de aparelhos que necessitem de abastecimento de água, como máquinas de lavar e de lavar louça. Os dados recolhidos serão usados para classificação dos aparelhos e modos de operação dos mesmos, em tempo real, permitindo fornecer relatórios sobre o consumo energético e modo de uso dos aparelhos, com grande grau de detalhe. Os relatórios podem incluir o uso de energia por vários ciclos de operação. Por exemplo, um aparelho pode executar vários ciclos de operação, como uma máquina de lavar que consume diferentes quantidades de energia elétrica e água consoante o modo de operação escolhido pelo utilizador. Toda a informação recolhida pode ser posteriormente utilizada em novos serviços de recomendação que ajudaram os utilizadores a definir melhor as configurações adequadas a um determinado dispositivo, minimizando o consumo energético e melhorando a sua eficiência. Adicionalmente toda esta informação pode ser utilizada para o diagnóstico de avarias e/ou manutenção preventiva. Em termos de proposta, o trabalho desenvolvido nesta tese tem as seguintes contribuições: Sistema de monitorização remota: o sistema de monitorização desenhado e implementado nesta tese avança o estado da arte dos sistemas de monitorização propostos pela literatura devido ao facto de incluir uma lista aprimorada de sensores que podem fornecer mais informações sobre os aparelhos, como o consumo de água da máquina de lavar. Além disso, é altamente flexível e pode ser implementado sem esforço em dispositivos novos ou antigos para monitorização de consumo de recursos. Conjunto de dados de consumo de energia de eletrodomésticos: Os dados recolhidos podem ser usados para futura investigação científica sobre o consumo de consumo de energia, padrões de uso de energia pelos eletrodomésticos e classificação dos mesmos. Abordagem de computação na borda (Edge Computing): O sistema de monitorização proposto explora o paradigma de computação na borda, onde parte da computação de preparação de dados é executada na borda, libertando recursos da nuvem para cálculos essenciais e que necessitem de mais poder computacional. Classificação precisa de dispositivos em tempo real: Coma proposta desenhada nesta tese, podemos classificar os dispositivos com alta precisão, usando os dados recolhidos pelo sistema de monitorização desenvolvido na tese. A abordagem proposta consegue classificar os dispositivos, que são monitorizados, com baixas taxas de falsos positivos. Para fácil compreensão do trabalho desenvolvido nesta tese, de seguida descreve-se a organização do documento. O Capítulo 1 apresenta o problema do consumo de energia na União Europeia e discute o aumento do consumo da mesma. O capítulo apresenta também os principais objetivos e contribuições do trabalho. No Capítulo 2 revê-se o trabalho relacionado em termos de sistema de monitorização remota, que inclui sensores, microcontroladores, processamento e filtragem de sinal. Por fim, este capítulo revê os trabalhos existentes na literatura relacionados com o problema de classificação de dispositivos usando abordagens de machine learning. No Capítulo 3 discutem-se os requisitos do sistema e o projeto de arquitetura conceitual do sistema. Neste capítulo é proposta uma solução de hardware, bem como, o software e firmware necessários à sua operação. Os algoritmos de machine learning necessários à classificação são também discutidos, em termos de configurações necessárias e adequadas ao problema que queremos resolver nesta tese. O Capítulo 4 representa a implementação de um protótipo que servirá de prova de conceito dos mecanismos discutidos no Capítulo 3. Neste capítulo discute-se também a forma de integração do protótipo na plataforma SATO. Com base na implementação feita, no Capítulo 5 especificam-se um conjunto de testes funcionais que permitem avaliar o desempenho da solução proposta e discutem-se os resultados obtidos a partir desses testes. Por fim, o Capítulo 6 apresenta as conclusões e o trabalho futuro que poderá ser desenvolvido partindo da solução atual.Energy consumption is daily growing in European Union (EU). One day it will become hardly sustainable. For this not to happen European Commission decided to implement a European Union Strategy, emphasizing two objectives: increasing energy efficiency and decarbonization. About 72% of all buildings in the EU are not adapted to be energy efficient. This problem encourages us to create solutions that would help assess the energy consumption of appliances at residential houses. In this thesis, we proposed a system that collects data using an intrusive load monitoring approach, where each appliance will have a dedicated monitoring rig to collect the energy consumption data. The proposed solution will help us understand the real-life consumption of each device being monitored and compare the laboratory measurements observed versus domestic consumption estimated by the energy consumption based on the EU energy efficiency labelling system. The system proposed detects device consumption patterns and recognize its brand, model and what actions that appliance is executing, e.g., program of washing in a washing machine. To achieve our goal, we designed a hardware solution capable of collecting sensor data, filtering and send it to a cloud platform (the SATO platform). Additionally, in the cloud, we have a Machine Learning solution that deals with the data and recognizes the appliance and its operation modes. This recognition allows drawing a device/settings profile, which can detect faults and create a recommendation service that helps users define the better settings for a specific appliance, minimizing energy consumption and improving efficiency. Finally, we examine our prototype approach of the system implemented for targeted objectives in this project report. The document describes the experiments that we did and the final results. Our results show that we can identify the appliance and some of its operation modes. The proposed approach must be improved to make the identification of all operation modes. However, the current version of the system shows exciting results. It can be used to support the design of a new labelling system where daily operation measures can be used to support the new classification system. This way, we have an approach that allows improving the energy consumption, making builds more efficient

An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data

A voltage and current measurement dataset for plug load appliance identification in households

This paper presents the Plug-Load Appliance Identification Dataset (PLAID), a labelled dataset containing records of the electrical voltage and current of domestic electrical appliances obtained at a high sampling frequency (30 kHz). The dataset contains 1876 records of individually-metered appliances from 17 different appliance types (e.g., refrigerators, microwave ovens, etc.) comprising 330 different makes and models, and collected at 65 different locations in Pittsburgh, Pennsylvania (USA). Additionally, PLAID contains 1314 records of the combined operation of 13 of these appliance types (i.e., measurements obtained when multiple appliances were active simultaneously). Identifying electrical appliances based on electrical measurements is of importance in demand-side management applications for the electrical power grid including automated load control, load scheduling and non-intrusive load monitoring. This paper provides a systematic description of the measurement setup and dataset so that it can be used to develop and benchmark new methods in these and other applications, and so that extensions to it can be developed and incorporated in a consistent manner

Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction

Recently, a growing interest has been dedicated towards developing and implementing low-cost energy efficiency solutions in buildings. Accordingly, non-intrusive load monitoring has been investigated in various academic and industrial projects for capturing device-specific consumption footprints without any additional hardware installation. However, its performance should be improved further to enable an accurate appliance identification from the aggregated load. This paper presents an efficient non-intrusive load monitoring framework that consists of the following main components: (i) a novel fusion of multiple time-domain features is proposed to extract appliance fingerprints; (ii) a dimensionality reduction scheme is introduced to be applied to the fused time-domain features, which relies on fuzzy-neighbors preserving analysis based QR-decomposition. The latter can not only reduce feature dimensionality, but it can also effectively decrease the intra-class distances and increase the extra-class distances of appliance features; and (iii) a powerful decision bagging tree classifier is implemented to accurately classify electrical devices using the reduced features. Empirical evaluations performed on three real datasets, namely ACS-F2, REDD and WHITED collected at different sampling rates have shown a promising performance, according to the accuracy and F1 score achieved using the proposed non-intrusive load monitoring system. Reported accuracy and F1 score have reached both 100% for the WHITED dataset, 99.79% and 99.76% for the REDD dataset, and up to 99.41% and 98.93% for the ACS-f2 dataset, respectively. The outstanding performance achieved using the proposed solution determines its effectiveness in collecting individual-appliance consumption data and in promoting energy saving behaviors. 2020 The AuthorsThis paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. All authors approved the version of the manuscript to be published.Scopu

A database for appliances real-life energy performance and consumption assessment

Trabalho de Projeto de Mestrado, Informática, 2021, Universidade de Lisboa, Faculdade de CiênciasNowadays, the reduction of energy consumption is a pressing matter for the European Union and one field that remains a large part of this energy contribution is the EU buildings and the energy consuming equipment inside. The use of energy labels, in order to assess the energy efficiency on European buildings, has proven to not be enough due to the difference between prediction consumption and real life energy consumption. The SATO platform aims to provide cloud-based self-assessment and optimization of buildings and equipment/appliances energy. It achieves this goal by collecting data about the energy performance of various devices, both legacy and new, through the use of sensors. One of the objectives of this platform is to develop the databases and its dashboards to store and visualize data on the energy consumption of building appliances. With this goal in mind, this thesis will be able to provide the users a comparison between the design predictions and the real live consumption. This comparison is done resorting to the EU energy label metrics, that are stored on the EPREL database. This database contains the energy efficiency information about the devices covered by the Energy labelling regulation, and it was created with the goal of providing the public with this information. The sensor data metrics are obtained by applying the formulas in the regulation on energy labelling for the respective device type. Addi tionally, since this thesis is meant to be integrated in the SATO platform, this databases and metrics will be used in the assessment and development of optimization algorithms for devices and buildings. The current implementation makes available two databases, one to store sensor data and another for metadata and metrics, as well as two different dashboards, one to show device real time consumption’s, consumption metrics and device meta-data, and another to provide the users with a simple comparison between the expected consumption and the real live consumption. Testing was done with two different device types, electronic displays and household refrigerators, with the expectations in mind of the real life consumption being above the displayed the EU energy label. The results ended up being different from the predicted, with the real live consumption obtained from the sensor data being the highest. This may be due to the low amount of collected data

non intrusive load monitoring using additive time series modeling via finite mixture models aggregation

Due to an exponential rise in energy consumption, it is imperative that buildings adopt sustainable energy consumption systems. A number of studies have shown that this can be achieved by providing real-time feedback on the energy consumption of each appliance to residents. It is possible to accomplish this through non-intrusive load monitoring (NILM) that disaggregates electricity consumption of individual appliances from the total energy consumption of a household. Research on NILM typically trains the inference model for a single house which cannot be generalized to other houses. In this Master thesis, a novel approach is proposed to tackle mentioned issue.This thesis proposes to use two finite mixture models namely generalized Gaussian mixture and Gamma mixture, to create a generalizable electrical signature model for each appliance type by training over labelled data and create various combinations of appliances together. By using this strategy, a model can be used on unseen houses, without extensive training on the new house. The issue of different measurement resolutions in the NILM area is also a considerable challenge. As a rule of thumb, state-of-the-art methods are studied using high-frequency data, which is rarely applicable in real-world situations due to smart meters' limited precision. To address this issue, the model is evaluated on three different datasets with different timestamps, AMPds, REDD and IRISE datasets. To increase the aggregation level and compare with RNN and FHMM as two well-known methods in NILM, an extension that we called DNN-Mixtures, is proposed. The results show that the proposed model can compete with state of art techniques. For evaluation, accuracy, precision, recall and F-score metrics are used

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável

Interactive visual study for residential energy consumption data

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions' responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic. 2022 The Author(s)This paper is made possible by National Priorities Research Program, Qatar (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu

Hardware and software platforms to deploy and evaluate non-intrusive load monitoring systems

The work in this PhD thesis addresses the practical implications of deploying and testing Non-Intrusive Load Monitoring (NILM) and eco-feedback solutions in real-world scenarios. The contributions to this topic are centered around the design and development of NILM frameworks that have been deployed in the wild, supporting long-term research in ecofeedback and also serving the purpose of producing real-world datasets and furthering the state of the art regarding the performance metrics used to evaluate NILM algorithms. This thesis consists of three main parts: i) the development of tools and datasets for NILM and eco-feedback research, ii) the design, implementation and deployment of NILM and eco-feedback technologies in real world scenarios, and iii) an experimental comparison of performance metrics for event detection and event classification algorithms. In the first part we describe the Energy Monitoring and Disaggregation Data Format (EMD-DF) and the SustData and SustDataED public datasets. In second part we discuss the development and deployment of two hardware and software platforms in real households, to support eco-feedback research. We then report on more than five years of experience in deploying and maintaining such platforms. Our findings suggest that the main practical issues can be divided in two categories, technological (e.g., system installation) and social (e.g., maintaining a steady sample throughout the whole study). In the final part of this thesis we analyze experimentally the behavior of a number of performance metrics for event detection and event classification, identifying clusters and relationships between the different measures. Our results evidence some considerable differences in the behavior of the performance metrics when applied to the different problems.O trabalho desenvolvido nesta tese de doutoramento aborda as implicações praticas da instalação e avaliação de soluções de monitorização não intrusiva de cargas elétricas (NILM) e eco-feedback em cenários reais. As contribuições para este tópico estão centradas em torno da concepção e desenvolvimento de plataformas NILM que foram instaladas em ambientes não controlados, suportando a pesquisa de longo termo em eco-feedback e servindo também o propósito de produzir conjuntos de dados científicos, bem como promover o avanço do estado da arte acerca das métricas de desempenho utilizadas para avaliar algoritmos NILM. Esta tese é constituída por três partes principais: i) o desenvolvimento de ferramentas e conjuntos de dados científicos para investigação em NILM e eco-feedback, ii) a concepção, desenho e instalação de tecnologias NILM e eco-feedback em cenários reais, e iii) uma comparação experimental de métricas de desempenho para algoritmos de detecção e de classificação de eventos. Na primeira parte descrevemos o Energy Monitoring and Disaggregation Data Format (EMD-DF) e os conjuntos de dados científicos SustData e SustDataED. Na segunda parte discutimos o desenvolvimento e instalação de duas plataformas de hardware e software em residências atuais com a finalidade de suportar a investigação em eco-feedback. Aqui, reportamos sobre mais de cinco anos de experiência na instalação e manutenção destes sistemas. Os nossos resultados sugerem que as principais implicações práticas podem ser divididas em duas categorias, físicas (e.g., instalação do sistema) e sociais (e.g., manter uma amostra constante ao longo de todo o estudo). Na terceira parte analisamos experimentalmente o comportamento de uma série de métricas de desempenho quando estas são utilizadas para avaliar algoritmos de detecção e de classificação de eventos. Calculamos as correlações lineares e não lineares entre os vários pares de métricas, e com base nesses valores procuramos agrupar as métricas que evidenciam um comportamento semelhante. Os nossos resultados sugerem a existência de diferenças evidentes no comportamento das métricas quando aplicadas a ambos dos problemas.Fundação para a Ciência e a Tecnologi