Characterizing residential load patterns on multi-time scales utilizing LSTM autoencoder and electricity consumption data

Abstract

• We propose a novel framework to characterize residential load patterns utilizing LSTM-AE and electricity consumption data. • LSTM-AE model is designed for dimensionality reduction and feature extraction. • Two-level clustering method is proposed to discover TLPs and MLPs on multi-time scales. • TLPs profile resident's load patterns from a global view, MLPs characterize load patterns of individual and groups. • Consumer groups and residential energy lifestyles are revealed to customize personal demand response strategies. Load patterns represent a clear picture of electricity usage, reflecting the consumer's habits. Previous works mainly focused on load patterns discovery on a fixed scale, but limited to characterize load patterns on multi-time scales utilizing electricity consumption data (ECD). Therefore, we propose a novel framework to characterize residential load patterns on multi-time scales. The long-short-term memory autoencoder (LSTM-AE) model is designed for dimensionality reduction and feature extraction. Furthermore, a two-level clustering method is proposed to discover and characterize typical load patterns (TLPs) and multifaceted load patterns (MLPs) on multi-time scales. The proposed framework is comprehensively evaluated via extensive experiments on three real ECD. Results show that: (1) Reconstruction errors of LSTM-AE are lower than 6 benchmark models across different time scales, which validates the superiority of LSTM-AE. (2) TLPs and MLPs on daily, weekly, monthly and yearly scale are discovered by the two-level clustering method. TLPs profile the resident's electricity usages from a global view. (3) MLPs present the consumer segmentation and characterize residential load patterns of individual and groups. Especially, customer groups and electricity usage habits or lifestyles are revealed thoroughly to customize personal demand response strategies. This study can provide new valuable insights for smart grid applications.