Hierarchical LSTM-Based Classification of Household Heating Types Using Measurement Data

Abstract

A lack of knowledge of the heating systems used by electricity consumers impedes distribution system operators in developing a sound grid upgrade plan and estimating potential demand flexibility from these consumers. The large-scale rollout of smart meters for electricity consumers provides an excellent opportunity to identify end users' heating types. This paper proposed a hierarchically structured deep-learning framework for identifying heating types of individual electricity consumers. The main contributions of the paper are: (a) We propose an effective framework based on long short-term memory (LSTM) that offers an effective automatic feature learning from sequential electricity consumption data and weather conditions. (b) We apply the proposed deep-learning architecture for household heating type classification which is among the first few successful reports on this application. We evaluate the performance using hourly measurement data collected over four years from one and two-family dwellings with either district heating, exhaust air heat pumps or direct electric heating as the heating type. Good performance was shown from the test results using the proposed framework, with an average test accuracy of 94.2%. Comparisons with four existing machine learning algorithms using handcrafted features and a single-layer LSTM-based deep-learning algorithm have shown marked improvement of the proposed method.