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

Appliance Identification in NILM Applications by means of a Convolutional Auto-Encoder

n energy efficiency applications, Non-Intrusive Load Monitoring techniques (NILM) are typically used to deduce which electrical loads are being used in a building at a given time. The identification of household appliances, in particular manually operated ones, is relevant information that can also be applied to infer the routines of tenants in Active and Assisted Living environments (AAL). These tools and applications are becoming increasingly interesting, especially in Western countries, where the ageing population is putting a strain on public social and health services. In this context, this work aims to classify the on/off events of the devices considered in the BLUED database. For this purpose, an architecture is presented, consisting of a Convolutional Auto-Encoder (CAE) followed by a classifier neural network. The CAE is used to implement a dimensionality reduction process after the encoder. Input data are formatted as images, created with extracted sections of the high-frequency electric current signal captured around the switching events. It is noteworthy that this dimensionality reduction also allows a decrease in the computational load of the classifier. Regarding the CAE functionality, the reconstruction error reaches a value of 1.579 · 10−3, whereas in the validation stage a weighted average classification F1-score of 87 % is obtained for the whole architecture

Comparison of Neural Networks for High-Sampling Rate NILM Scenario

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 22-24 June 2022, Messina, Italy.The common objective of techniques employed to identify the use of household appliances is related to energy efficiency and the reduction of energy consumption. In addition, through load monitoring it is possible to assess the degree of independence of tenants with minimal invasion of privacy and thus develop sustainable health systems capable of providing the required services remotely. Both approaches should initially deal with the load identification stage. For that purpose, this work presents three different solutions that take the events of the electrical current signal acquired at high frequency and process them for classification by using two different topologies of Artificial Neural Networks (ANN). The data of interest used as input for the ANN in the proposals are the normalized signal captured around the events, the images created by dividing that signal into sections and organizing them in a matrix, and the images coming from the Short Time Fourier Transform (STFT) of the signal around the event. The dataset BLUED is used to carry out the validation of the proposal, where some of the proposed architectures obtain an F1 score above 90% for more than fifteen devices under classification.Universidad de AlcaláAgencia Estatal de Investigació

Evaluating Human Activity and Usage Patterns of Appliances with Smart Meters

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 22-24 June 2022, Messina, Italy.Population ageing is becoming a key issue for most western countries, due to the challenges that it poses to the sustainability of future healthcare systems. In this context, many proposals and development are emerging trying to enhance the independent living of elderly and cognitive impaired people at their own homes. For that purpose, the massive deployment of smart meter at houses and buildings, initially focused on improving the energy management, has become a useful tool to provide the society with a variety of services and applications that can be employed for independent living. This work proposes the use of a commercial smart meter that delivers the disaggregated consumption per appliance every hour. This device has been installed on a test house during a training period of two months, in order to infer the behavior routines in the usage of the microwave. After the training, every new day can be compared to the obtained usage pattern of that appliance, in order to launch a notification when the day routine significantly differs. Similarly, since the use of the microwave is related to cooking, activities such as breakfast, lunch or dinner, may also be monitored and/or compared to a trained pattern. The proposal has been validated preliminary with experimental data coming from the aforementioned household.Agencia Estatal de InvestigaciónUniversidad de Alcal

Definition of NILM techniques for energy disaggregation in AIIL environments

Trabajo Fin de Grado (TFG)El proyecto se centra en la definición y el desarrollo de técnicas NILM (NonIntrusive Load Monitoring) para sistemas de monitorización que promueven la vida independiente. Los algoritmos desarrollados en este proyecto abordarán el problema de la desagregación de energía y la identificación de electrodomésticos. A través del procesado de la señal de la corriente se obtendrán los eventos provocados por los distintos electrodomésticos que, para poder realizar la identificación, serán clasificados a través de una red neuronal. Las diferentes técnicas propuestas se evaluarán mediante el uso de bases de datos sobre el consumo de energía de diversos hogares bajo estudio.The project focuses on the definition and development of Non-intrusive Load Monitoring (NILM) techniques for monitoring systems that promote independent living. The algorithms developed in this project will address the problem of disaggregation of energy and the identification of household appliances. Through the processing of the current signal, the events caused by the different household appliances will be obtained, which, in order to carry out the identification, will be classified through a neural network. The different proposed techniques will be evaluated by using the energy consumption samples available in some databases from various homes under study.Grado en Ingeniería en Electrónica y Automática Industria