Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants

Abstract

Effective electricity consumption forecasting is extremely significant for enterprises’ electricity planning which can provide data support for production decision, thus improving the level of enterprises' clean production. In recent years, recurrent neural network (RNN) and its variants have led to extensive research for time series forecasting. However, the performance and selection of these models in enterprise electricity forecasting have not been reported. With this study, we attempted to back some of these solutions with experimental results. This paper focused on a comparison for daily enterprise electricity consumption forecasting using different RNN models, i.e, standard RNN, long short-term memory-based RNN (LSTM), and gated recurrent unit-based RNN (GRU). To test their regression performance, three Chinese enterprises with different scales of electricity consumption are investigated. The comparison results show that the LSTM and the GRU models are slightly better than that of the RNN in terms of normalized root-mean-square error, mean absolute percentage error and threshold statistic. Moreover, the GRU model with the simplest structure is significantly different from the RNN, but not from LSTM in terms of Friedman testing. Hence the GRU model can be regarded as the first candidate for the enterprise electricity consumption forecasting in the future work.