Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

Abstract

In this paper, we propose a hybrid forecasting model (HFM) for the short-term electric load forecasting using artificial neural network (ANN), discrete Fourier transformation (DFT) and principal component analysis (PCA) techniques in order to attain higher prediction accuracy. Firstly, we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation. Approximate curves, together with other input variables, are given to the ANN to predict the next-day hourly load curves. Furthermore, we predict PCA scores to obtain approximate load curves in the first step, which are then given to the ANN again in the second step. Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads. Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018, we train our models using historical data since January 2008. The forecast results show that the HFM consisting of “ANN with DFT” and “ANN with PCA” predicts next-day hourly loads more accurately than the conventional three-layered ANN approach. Their corresponding mean average absolute errors show 2.7% for ANN with DFT, 2.6% for ANN with PCA and 3.0% for the conventional ANN approach. We also find that in May, when electric demand is smaller with smaller fluctuations, forecasting errors are much smaller than January for all the models. Thus, we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.