Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring

Identifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including discrete wavelet transform, low-pass filter, and seasonality decomposition, to find the best filtering method for disaggregating different flexible loads (e.g., heat pumps). Experimental results are presented for estimating the electricity consumption of a heat pump, a refrigerator, and a dishwasher from the total power of a residential house in British Columbia (a publicly available use case). The results show that AEFLSTM can reduce the loss error (mean absolute error) by 57.4%, 44%, and 55.5% for estimating the power consumption of the heat pump, refrigerator, and dishwasher, respectively, compared to the stand-alone LSTM model. The proposed approach is used for another dataset containing measurements of an electric vehicle to further support the validity of the method. AEFLSTM is able to improve the result for disaggregating an electric vehicle by 22.5%

Otimização aplicada ao monitoramento não intrusivo de cargas elétricas residenciais

Orientador: Marcos Julio Rider FloresDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta um método de monitoramento não intrusivo (Non-Intrusive Load Monitoring - NILM) baseado em programação linear inteira mista (Mixed-Integer Linear Programming - MILP). NILM são métodos para desagregar leituras de medidores de energia em informações a respeito dos aparelhos eletrodomésticos em operação. Tais informações, como consumo e estado de operação, são valiosas para promover a eficiência energética e manutenção preventiva. A técnica NILM proposta neste trabalho expande o modelo clássico fundamentado em otimização combinatória (Combinatorial Optimization - CO). A nova formulação lida com o problema de ambiguidade de cargas similares, presente no modelo clássico. Restrições lineares são utilizadas para representar eficientemente as assinaturas de carga. Além disso, uma estratégia baseada em janelas temporais é proposta para melhorar o desempenho computacional. A desagregação de cargas pode ser feita utilizando apenas medidas de potência ativa em uma baixa taxa de amostragem, disponível em medidores inteligentes comerciais. A técnica também permite a utilização de outros tipos de medidas, se disponíveis, como a potência reativa. O desempenho do algoritmo é validado utilizando dois casos de teste a partir da base de dados pública AMPds. A taxa de amostragem do caso de teste é de uma amostra por minuto. Os resultados demonstram a habilidade do método proposto para identificar e desagregar com precisão as assinaturas de energia individuais de forma computacionalmente eficienteAbstract: This work presents a non-intrusive load monitoring (NILM) method based on mixed-integer linear programming (MILP). NILM are methods for disaggregating measurements from energy meters into information regarding operating appliances. Such information, such as the power consumption and operating state, are valuable for promoting energy savings and predictive maintenance. The proposed technique expands the classical model based on combinatorial optimization (CO). The new formulation handles the problem of ambiguity of similar loads, present in the classical model. Linear constraints are used to efficiently represent load signatures. Additionally, a window-based strategy is proposed to enhance the computational performance of the proposed NILM algorithm. The disaggregation can be made using only active power measurements at a low sampling rate, which is already available in commercial smart meters. Other features can be added to the model, if available, such as the reactive power. The performance of the algorithm is evaluated using two test cases from the public dataset AMPds. The sampling rate from the test case is of one sample per minute. Results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy signatures in a computationally efficient wayMestradoEnergia ElétricaMestre em Engenharia Elétric

Deep learning applications in non-intrusive load monitoring

Non-Intrusive Load Monitoring (NILM) is a technique for inferring the power consumption of each appliance within a home from one central meter, aiding in energy conservation. In this thesis I present several Deep Learning solutions for NILM, starting with two preliminary works – A proof of concept project for multisensory NILM on a Raspberry Pi; and a fully developed NILM solution named WaveNILM. Despite their success, both methods struggled to generalize outside their training data, a common problem in NILM. To improve generalization, I designed a framework for synthesizing truly novel appliance level power signatures based on generative adversarial networks (GAN) – the main project of this thesis. This generator, named PowerGAN, is trained using a variety of GAN techniques. I present a comparison of PowerGAN to other data synthesis work in the context of NILM and demonstrate that PowerGAN is able to create truly synthetic, realistic, diverse, appliance power signatures

An optimisation-based energy disaggregation algorithm for low frequency smart meter data

An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives