ABSTRACT
This study proposes a new convolutional neural network (CNN) method with an input-signal decomposition algorithm. With the proposed CNN architecture, hourly electricity consumption data for the Covid-19 period in Turkey were used as input data, and the short-term electricity consumption was forecasted. The input data were decomposed into its subcomponents using a signal decomposition process called Empirical Mode Decomposition (EMD). To extract the deep features, all input data were transformed into 2D feature maps and fed into the CNN. The obtained results were compared with the pre-trained models GoogleNet, AlexNet, SqueezeNet, and ResNet18. Model-wise comparisons showed that the proposed method had the highest correlation coefficient (R) and lowest root mean square error (RMSE) and mean absolute error (MAE) values for 1-h, 2-h, and 3-h. The mean R-values of the proposed method were 95.6%, 95.2%, and 94.0% for 1h, 2h and 3h ahead, respectively. The mean RMSE values were 8.2%, 8.7%, and 10.2% for 1h, 2h and 3h ahead, respectively. The experimental results confirm that the proposed method outperforms other pretrained methods despite its simpler structure. © 2013 IEEE.