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

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

ICASSP 2019accepted0 citations

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.

BibTeX

@inproceedings{icassp2019_bayesianoptimize,
  title = {Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring},
  author = {Maria Kaselimi and Nikolaos Doulamis and Anastasios D. Doulamis and Athanasios Voulodimos and Eftychios Protopapadakis},
  booktitle = {ICASSP 2019},
  year = {2019}
}