A short-term individual residential load forecasting method based on deep learning and k-means clustering
Fujia Han, Tianjiao Pu, Maozhen Li, Gareth Taylor
Abstract
In order to currently motivate a wide range of various interactions between power network operators and electricity customers, residential load forecasting plays an increasingly important role in demand side response (DSR). Due to high volatility and uncertainty of residential load, it is significantly challenging to forecast it precisely. Thus, this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering, which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level. It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load. The presented method is tested and validated on a real-life Irish residential load dataset, and the experimental results suggest that it can achieve a much higher prediction accuracy, in comparison with a published benchmark method.