Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection

The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.This research was funded by Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020, grant numbers 39578/2018 and 72581/2020. Antonio Ruano also acknowledges the support of Fundação para a Ciência e Tecnologia, grant UID/EMS/50022/2020, through IDMEC under LAETAinfo:eu-repo/semantics/publishedVersio

Energy consumption disaggregation in residences with photovoltaic generation

Orientador: Luiz Carlos Pereira da SilvaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A compreensão da fatura de energia elétrica ocorre, muitas vezes, somente naquele momento quando a expectativa de custo não alcança o esperado para aquele mês. Mesmo lendo e relendo a conta sempre vem aquela pergunta: `para onde foi tanta energia?¿. Ainda assim, mesmo sem dados suficientes, os usuários começam aquela busca pelos motivos de tanto gasto "inesperado". Neste sentido, com o objetivo de melhorar a informação recebida pelo consumidor, propondo-lhe eficiência energética através de informes relevantes, como por exemplo: o consumo por equipamento, quais eletrodomésticos estão obsoletos ou com mal funcionamento e dicas de consumo eficiente; a UNICAMP em conjunto com a CPFL Paulista (distribuidora de energia elétrica) firmaram um projeto de Pesquisa e Desenvolvimento, intitulado: "PA3020 - Desagregação do Consumo de Energia Elétrica em Consumidores Residenciais"; que visa estudar e propor métodos de monitoramento do consumo de energia que possibilitem o detalhamento do uso por carga elétrica e seu comportamento. Dentre os métodos já estudados, o mais promissor é o monitoramento concentrado ou monitoramento não intrusivo (NILM em inglês), baseado em uma única medição das grandezas elétricas, diretamente no ramal de entrada da residência. Neste contexto e inserido no projeto mencionado, este trabalho visa demonstrar a arquitetura de desagregação desenvolvida no PA3020 e seus resultados no contexto residencial, porém acrescentando a geração solar fotovoltaica (FV) como variável. Por ser um tema pouco comentado na literatura e de grande relevância, tendo em vista o crescimento da geração distribuída, analisar os possíveis impactos de ambas tecnologias na mesma residência (NILM e geração FV) se tornam importantes no contexto das smart grids. Para isso, este projeto se baseou no uso de uma plataforma de desagregação, desenvolvida no PA3020, que contempla: bancos de dados de curvas de carga, de 10 residências, sendo uma com geração FV; metodologias NILM (detecção de eventos, reconhecimento de eventos e estimação do consumo); base de dados de assinaturas elétricas de equipamentos, totalizando mais de 100 aparelhos medidos; e um cliente web para visualização das curvas de cargas e resultados de simulações. As análises foram realizadas em três estágios: o primeiro, compreende na verificação do impacto da geração FV na curva de carga e na validação dos métodos de detecção de eventos liga/desliga de equipamentos; subsequentemente, o segundo estágio explora a detecção em conjunto com a fase de reconhecimento de eventos; e por último, no terceiro estágio temos a compilação dos resultados anteriores com a estimação do consumo. Como esperado, as simulações demonstram uma correlação entre a desagregação e as condições climáticas da geração FV, isto é, em dias com poucas nuvens é possível detectar as cargas de maior potência e com alta representatividade no consumo final. Porém, conforme o aumento de nuvens e chuvas, a assertividade da desagregação diminui no período da geração FV. Concluindo, este trabalho apresenta uma revisão analítica sobre o tema de desagregação do consumo de energia elétrica, de maneira não intrusiva, com levantamento da influência da geração solar fotovoltaica na decomposição da curva de carga total em equipamentos eletrodomésticosAbstract: Understanding the electricity bill often occurs only when cost expectation did not reach the prospective for that month. Even reading and re-reading the account, we always come up with the question: 'where was spent so much energy?' Still, even without enough data, users begin to search the reasons of those "unexpected" spending. In this regard, in order to improve the information received by the consumer, offering energy efficiency through relevant reports, such as: equipment consumption, which appliances are obsolete or malfunctioning and efficient consumer tips; UNICAMP together with CPFL Paulista (utility company) signed a Research and Development project, entitled: "PA3020 - Electricity Consumption Breakdown in Residential Consumers"; which aims to study and propose energy consumption monitoring methods that allow the detailed use of electric charge and its behaviour. Among the methods already studied, the most promising is concentrated monitoring or non-intrusive load monitoring (NILM), based on a single smart meter, directly at the residence entrance branch. In this context and inserted in the mentioned project, this work aims to demonstrate the disaggregation architecture developed in PA3020 and its results in residential context but adding solar photovoltaic (PV) generation as a new variable. Being a subject, little discussed in literature and with great importance in view of distributed generation growth, the analysis of the technology¿s potential impacts in same household (NILM and PV generation) become important in smart grids context. So, this project was based on a disaggregation platform, developed in PA3020, which includes: load curves databases, in 10 residences, one with PV generation; NILM methodologies (event detection, event recognition and consumption estimation); electrical equipment signatures database, totalling more than 100 measured devices; and a web client for visualizing load curves and simulation results. The analyses were carried out in three stages: the first one comprises the verification of PV generation impact on load curve and equipment on/off event detection methods validation; subsequently, the second stage exploits the detection in conjunction with the event recognition phase; and finally, in third stage we have the compilation of previous results with consumption estimation. As expected, the simulations demonstrate a correlation between the disaggregation and the climatic conditions of the PV generation, that is, in days with few clouds it is possible to detect the loads of with high power demand and with high representativeness in the final consumption. However, as cloud and rainfall increases, the assertiveness of the disaggregation decreases during the PV generation period. In conclusion, this work presents an analytical review on the subject of power consumption disaggregation, non-intrusive way, a survey of the solar photovoltaic generation influence in the decomposition of total load curve apparatuses equipmentMestradoEnergia EletricaMestre em Engenharia ElétricaCAPE

OSEM : occupant-specific energy monitoring.

Electricity has become prevalent in modern day lives. Almost all the comforts people enjoy today, like home heating and cooling, indoor and outdoor lighting, computers, home and office appliances, depend on electricity. Moreover, the demand for electricity is increasing across the globe. The increasing demand for electricity and the increased awareness about carbon footprints have raised interest in the implementation of energy efficiency measures. A feasible remedy to conserve energy is to provide energy consumption feedback. This approach has suggested the possibility of considerable reduction in the energy consumption, which is in the range of 3.8% to 12%. Currently, research is on-going to monitor energy consumption of individual appliances. However, various approaches studied so far are limited to group-level feedback. The limitation of this approach is that the occupant of a house/building is unaware of his/her energy consumption pattern and has no information regarding how his/her energy-related behavior is affecting the overall energy consumption of a house/building. Energy consumption of a house/building largely depends on the energy-related behavior of individual occupants. Therefore, research in the area of individualized energy-usage feedback is essential. The OSEM (Occupant-Specific Energy Monitoring) system presented in this work is capable of monitoring individualized energy usage. OSEM system uses the electromagnetic field (EMF) radiated by appliances as a signature for appliance identification. An EMF sensor was designed and fabricated to collect the EMF radiated by appliances. OSEM uses proximity sensing to confirm the energy-related activity. Once confirmed, this activity is attributed to the occupant who initiated it. Bluetooth Low Energy technology was used for proximity sensing. This OSEM system would provide a detailed energy consumption report of individual occupants, which would help the occupants understand their energy consumption patterns and in turn encourage them to undertake energy conservation measures

Distribution Level Building Load Prediction Using Deep Learning

Load prediction in distribution grids is an important means to improve energy supply scheduling, reduce the production cost, and support emission reduction. Determining accurate load predictions has become more crucial than ever as electrical load patterns are becoming increasingly complicated due to the versatility of the load profiles, the heterogeneity of individual load consumptions, and the variability of consumer-owned energy resources. However, despite the increase of smart grids technologies and energy conservation research, many challenges remain for accurate load prediction using existing methods. This dissertation investigates how to improve the accuracy of load predictions at the distribution level using artificial intelligence (AI), and in particular deep learning (DL), which have already shown significant progress in various other disciplines. Existing research that applies the DL for load predictions has shown improved performance compared to traditional models. The current research using conventional DL tends to be modeled based on the developer\u27s knowledge. However, there is little evidence that researchers have yet addressed the issue of optimizing the DL parameters using evolutionary computations to find more accurate predictions. Additionally, there are still questions about hybridizing different DL methods, conducting parallel computation techniques, and investigating them on complex smart buildings. In addition, there are still questions about disaggregating the net metered load data into load and behind-the-meter generation associated with solar and electric vehicles (EV). The focus of this dissertation is to improve the distribution level load predictions using DL. Five approaches are investigated in this dissertation to find more accurate load predictions. The first approach investigates the prediction performance of different DL methods applied for energy consumption in buildings using univariate time series datasets, where their numerical results show the effectiveness of recursive artificial neural networks (RNN). The second approach studies optimizing time window lags and network\u27s hidden neurons of an RNN method, which is the Long Short-Term Memory, using the Genetic Algorithms, to find more accurate energy consumption forecasting in buildings using univariate time series datasets. The third approach considers multivariate time series and operational parameters of practical data to train a hybrid DL model. The fourth approach investigates parallel computing and big data analysis of different practical buildings at the DU campus to improve energy forecasting accuracies. Lastly, a hybrid DL model is used to disaggregate residential building load and behind-the-meter energy loads, including solar and EV