A novel Monte Carlo-based neural network model for electricity load forecasting. (5th May 2020)

• Record Type: Journal Article
• Authors: Yong, Binbin; Xu, Zijian; Shen, Jun; Chen, Huaming; Wu, Jianqing; Li, Fucun; Zhou, Qingguo
• Is Part Of: International journal of embedded systems. Volume 12:Number 4(2020)
• Page Start: 522
• Page End: 533
• Publication Date: 2020-05-05
• Subjects: electricity load forecasting -- general vector machine -- GVM -- time series prediction -- neural network; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated their great advantages. General vector machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we apply it in electricity load forecasting. A detailed comparison with traditional back-propagation neural network (BP) is presented in this paper. To improve the load forecasting accuracy, we propose many methods to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting.