GRU-based Encoder-Decoder for Short-term CHP Heat Load Forecast

Abstract

A gated recurrent units (GRU) network based encoder-decoder (E-D) model is proposed for combined heat and power (CHP) heat load forecasting. First, we use the GRU based E-D model as an auto-encoder to map the historical CHP heat load time series into a fixed length representation. Then, concatenate the representation with weather forecasting information as a new input to another multiple GRUs network for heat load prediction. Data collected from Rizhao, Shandong province is used to verify the conclusions. Results illustrate that GRU-based E-D model can give a more accurate forecast to the short-term CHP heat load compared with non-auto-encoded RNN model.