Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization

This paper looks at the extraction of trends of household electrical seasonal consumption via load disaggregation. With the proviso that data for several home devices can be embedded in a tensor, non-negative multi-way array factorization is performed in order to extract the most relevant components. In the initial decomposition step the decomposed signals are incorporated in the test signal consisting of the whole-home measured consumption. After this the disaggregated data corresponding to each electrical device is obtained by factorizing the associated matrix through the learned model. Finally, we evaluate the performance of load disaggregation by the supervised method, and study the trends along several years and across seasons. Towards this end, computational experiments were yielded using real-world data from household electrical consumption measurements along several years. While breaking down the whole house energy consumption into appliance level gives less accurate estimates in the late years, we empirically show the adequacy of this method for handling the earlier years and the estimates of the underlying seasonal trend-cycle.info:eu-repo/semantics/acceptedVersio

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

Bayesian Matrix Factorization and Applications

Nonnegative matrix factorization (NMF) reduces the observed nonnegative matrix into a product of two nonnegative matrices. Nonnegativity entails two major implications: non-negative components and purely additive combination. These characteristics made this method useful in a wide range of applications. In this thesis, we propose two novel Bayesian nonnegative matrix factorization techniques. First, we propose a model dedicated to semi-bounded data where each entry of the observed matrix is supposed to follow an Inverted Beta distribution. Latent variables of the factorized parameter matrices follow a Gamma prior. Variational Bayesian inference and lower bound approximation for the objective function are used to find an analytically tractable solution for the model. An online extension of the algorithm is also proposed for more scalability. Both models are evaluated on five different applications. Second, we propose a Bayesian NMF that can be specifically useful for non intrusive load monitoring (NILM). NILM can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. The model achieves superior performance by imposing sparsity on the activation matrix using Dirichlet priors. To estimate the parameters of the model, variational Bayesian inference is used. A novel optimization approach is proposed to find an analytically tractable solution for the model. We evaluate the model with three data sets: REDD, AMPds and IRISE, and with multiple experimental setups. The proposed model provides interpretability, flexibility and high performance

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset

Austrian High-Performance-Computing meeting (AHPC2020)

This booklet is a collection of abstracts presented at the AHPC conference