Energy Disaggregation for Real-Time Building Flexibility Detection

TL;DR

This paper explores energy disaggregation techniques to detect real-time building flexibility, comparing classifiers and using feature extraction to improve accuracy, achieving over 96% accuracy on real appliance data.

Contribution

It introduces a novel approach combining feature extraction with classifiers for energy disaggregation, enhancing real-time building flexibility detection.

Findings

Support Vector Machine and AdaBoost achieved high accuracy.

Feature extraction with Restricted Boltzmann Machine improved classifier performance.

Method demonstrated robustness with over 96% accuracy on real data.

Abstract

Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.