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

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  • Xianfei Yang
  • Xiang Yu
  • Hui Lu

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.

Suggested Citation

  • Xianfei Yang & Xiang Yu & Hui Lu, 2020. "Dual possibilistic regression models of support vector machines and application in power load forecasting," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720921636
    DOI: 10.1177/1550147720921636
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    References listed on IDEAS

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    1. Guo, Peijun & Tanaka, Hideo, 2006. "Dual models for possibilistic regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 253-266, November.
    2. Chapaloglou, Spyridon & Nesiadis, Athanasios & Iliadis, Petros & Atsonios, Konstantinos & Nikolopoulos, Nikos & Grammelis, Panagiotis & Yiakopoulos, Christos & Antoniadis, Ioannis & Kakaras, Emmanuel, 2019. "Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system," Applied Energy, Elsevier, vol. 238(C), pages 627-642.
    3. Tanaka, Hideo & Hayashi, Isao & Watada, Junzo, 1989. "Possibilistic linear regression analysis for fuzzy data," European Journal of Operational Research, Elsevier, vol. 40(3), pages 389-396, June.
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