Dual possibilistic regression models of support vector machines and application in power load forecasting

Xianfei Yang, Xiang Yu and Hui Lu

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 5, 1550147720921636

Abstract: Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Keywords: Interval data; dual possibilistic regression models; fuzzy regression analysis; support vector machine; quadratic programming problem; power load (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720921636

DOI: 10.1177/1550147720921636

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