Convolutional and recurrent neural network based model for short-term load forecasting.

• The electrical load series is analyzed in a two dimensional matrix form. • Univariate load and temperature data are expanded to multidimensional features. • 2-D convolutional layers are used for hidden feature extraction. • A network containing LSTM and GRU units is proposed for load forecasting. The consumed electrical load is affected by many external factors such as weather, season of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high accurate forecasting model for hourly load consumption with considering these external variables. At first, the electrical load and temperature time series are rearranged into separate two-dimensional matrices. Convolutional neural networks (CNNs) are utilized to extract the load and temperature features. The autocorrelation coefficients of the load and temperature sequences are used to determine the kernel size of the convolutional layers. At this stage, the convolutional layers specifically convert the univariate data to multidimensional features by applying two-dimensional convolutional kernels, which potentially increase the forecasting capability of recurrent neural networks. On the other hand, long short term memory (LSTM) and gated recurrent unit (GRU) are able to hold short-term and long-term memories. Therefore, in the next stage, the multidimensional features extracted by 2-D CNNs are fed as input to the bidirectional propagating GRU and LSTM units to perform hourly electrical load forecasting. The results of experiments on two datasets show the superiority of the proposed method compared to some recent works in the field of short-term load forecasting. [ABSTRACT FROM AUTHOR]