| Abstract: | Recently, a pattern sequence‐based forecasting (PSF) algorithm was proposed for day‐ahead electricity time series. PSF consists of two steps: clustering and prediction. However, it has the following limitations: In the clustering step, it is computationally expensive to determine the optimal number of clusters with majority votes. In the prediction step, it is quite complex to search for the matched pattern sequence with the optimal window length, and averaging all the samples immediately after the matched sequence can increase the forecasting accuracy especially when the day under examination is a working day. In this paper, we propose a time‐series forecasting method for electricity load by addressing the limitations in PSF. The proposed method is evaluated on electricity load datasets, and the experimental results show that the proposed method can improve the forecasting accuracy of PSF. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
