Title: A novel Monte Carlo-based neural network model for electricity load forecasting

Authors: Binbin Yong; Zijian Xu; Jun Shen; Huaming Chen; Jianqing Wu; Fucun Li; Qingguo Zhou

Addresses: School of Information Science and Engineering, Lanzhou University, South Tianshui Road 222, Lanzhou 730000, China ' School of Information Science and Engineering, Lanzhou University, South Tianshui Road 222, Lanzhou 730000, China ' School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia ' School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia ' School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia ' School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia ' School of Information Science and Engineering, Lanzhou University, South Tianshui Road 222, Lanzhou 730000, China

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.

Keywords: electricity load forecasting; general vector machine; GVM; time series prediction; neural network.

DOI: 10.1504/IJES.2020.107631

International Journal of Embedded Systems, 2020 Vol.12 No.4, pp.522 - 533

Received: 06 May 2017
Accepted: 06 Jan 2018

Published online: 05 May 2020 *

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