Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring

Maria Kaselimi, Nikolaos Doulamis, Anastasios D. Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis

Abstract: In this paper, a Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, is introduced. Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a process aiming to identify the individual contribution of appliances in the aggregate electricity load. The proposed model, Bayes-BiLSTM, is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increase. In addition, a non-causal model is introduced in order to tackle with inherent structure, characterizing the operation of multi-state appliances. Furthermore, a Bayesian-optimized framework is introduced to select the best configuration of the proposed regression model, thus increasing performance. Experimental results indicate the proposed method's superiority, compared to the current state-of-the-art.