Forecasting Building Electricity Power Consumption Using Deep Learning Approach

Abstract

As electricity power usage increases in buildings, it is important to use and supply electricity power efficiently. Recently, there are studies to forecast the energy consumption by using the deep learning method, which can deal with time-series data. In this paper, we compare several deep learning methods to forecast the electricity power consumption in buildings. More specifically, we utilize the vanilla LSTM/GRU with multiple layers, the sequence-to-sequence model, and the sequence-to-sequence model with attention mechanisms. In the experiments, the LSTM/GRU models achieved good overall performance in the RMSE metrics. However, the variations of sequence-to-sequence models showed low performance due to the variation of floating number. Also, in a graphical viewpoint, the LSTM/GRU models well follow the trends of electricity power consumption, but the variation of sequence-to-sequence models can not follow the electricity power consumption pattern well.