Load forecasting based on deep neural network and historical data augmentation

Load forecasting is a complex non-linear problem with high volatility and uncertainty. This study presents a novel load forecasting method known as deep neural network and historical data augmentation (DNN-HDA). The method utilises HDA to enhance regression by DNN for monthly load forecasting, considering that the historical data to have a high correlation with the corresponding predicted data. To make the best use of the historical data, one year's historical data is combined with the basic features to construct the input vector for a predicted load. In this way, if there is C years' historical data, one predicted load can have C input vectors to create the same number of samples. DNN-HDA increases the number of training samples and enhances the generalisation of the model to reduce the forecasting error. The proposed method is tested on daily peak loads from 2006 to 2015 of Austria, Czech and Italy. Comparisons are made between the proposed method and several state-of-the-art models. DNN-HDA outperforms DNN by 44%, 38% and 63% on the three data sets, respectively.