Enhanced LSTM-based community energy consumption prediction model leveraging shared building cluster datasets

Abstract

Unlike in the past, as the scale of buildings expands from a single building to a group of buildings, it is necessary to respond to the demand for energy consumption for the entire community. This study attempted to improve the electricity consumption prediction performance of single buildings by reflecting the inherent occupant behaviour patterns embedded in the shared electricity consumption data of single buildings. The proposed method utilized electricity consumption data for all other 7 target buildings as a part of LSTM model inputs in combination with meteorological data to predict the electricity consumption profile of a specific building. As a result, the proposed method reduced the error by utilizing the similar energy profiles of the surrounding buildings, *and the prediction performance of the total electricity consumption of the community was increased by 5.11% based on RMSE. The newly proposed method reflects the inherent occupant behaviour patterns and temporal electricity consumption patterns from energy profiles of nearby buildings.