Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models.

Sammanfattning: The increasing energy consumption is one of the greatest environmental challenges of our time. Residential buildings account for a considerable part of the total electricity consumption and is further a sector that is shown to have large savings potential. Non Intrusive Load Monitoring (NILM), i.e. the deduction of the electricity consumption of individual home appliances from the total electricity consumption of a household, is a compelling approach to deliver appliance specific consumption feedback to consumers. This enables informed choices and can promote sustainable and cost saving actions. To achieve this, accurate and reliable appliance load disaggregation algorithms must be developed. This Master's thesis proposes a novel approach to tackle the disaggregation problem inspired by state of the art algorithms in the field of speech recognition. Previous approaches, for sampling frequencies 1 Hz, have primarily focused on different types of hidden Markov models (HMMs) and occasionally the use of artificial neural networks (ANNs). HMMs are a natural representation of electric appliances, however with a purely generative approach to disaggregation, basically all appliances have to be modelled simultaneously. Due to the large number of possible appliances and variations between households, this is a major challenge. It imposes strong restrictions on the complexity, and thus the expressiveness, of the respective appliance model to make inference algorithms feasible. In this thesis, disaggregation is treated as a factorisation problem where the respective appliance signal has to be extracted from its background. A hybrid model is proposed, where a convolutional neural network (CNN) extracts features that correlate with the state of a single appliance and the features are used as observations for a hidden semi Markov model (HSMM) of the appliance. Since this allows for modelling of a single appliance, it becomes computationally feasible to use a more expressive Markov model. As proof of concept, the hybrid model is evaluated on 238 days of 1 Hz power data, collected from six households, to predict the power usage of the households' washing machine. The hybrid model is shown to perform considerably better than a CNN alone and it is further demonstrated how a significant increase in performance is achieved by including transitional features in the HSMM.