A robust support vector regression model for electric load forecasting

Jian Luo, Tao Hong, Zheming Gao and Shu-Cherng Fang

International Journal of Forecasting, 2023, vol. 39, issue 2, 1005-1020

Abstract: Electric load forecasting is a crucial part of business operations in the energy industry. Various load forecasting methods and techniques have been proposed and tested. With growing concerns about cybersecurity and malicious data manipulations, an emerging topic is to develop robust load forecasting models. In this paper, we propose a robust support vector regression (SVR) model to forecast the electricity demand under data integrity attacks. We first introduce a weight function to calculate the relative importance of each observation in the load history. We then construct a weighted quadratic surface SVR model. Some theoretical properties of the proposed model are derived. Extensive computational experiments are based on the publicly available data from Global Energy Forecasting Competition 2012 and ISO New England. To imitate data integrity attacks, we have deliberately increased or decreased the historical load data. Finally, the computational results demonstrate better accuracy of the proposed robust model over other recently proposed robust models in the load forecasting literature.

Keywords: Cybersecurity; Electric load forecasting; Support vector regression; Data integrity attacks; Weight function (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:1005-1020

DOI: 10.1016/j.ijforecast.2022.04.001

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