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Convolutional and Recurrent Neural Network Based Model for Short-term Load Forecasting by Hosein Eskandari; Maryam Imani; Mohsen Parsa Moghaddam is a Engineering article available to read on EtoBox.

What is Convolutional and Recurrent Neural Network Based Model for Short-term Load Forecasting about?

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.

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Author

Hosein Eskandari; Maryam Imani; Mohsen Parsa Moghaddam

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Engineering (Physical Sciences)