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.

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支持向量机的双可能性回归模型及其在电力负荷预测中的应用

电力负荷预测是电力系统安全、稳定、经济运行的重要保障。用区间数据来表示电力负荷预测中的模糊信息是合适的。双可能性回归模型分别从外向和内向逼近观测区间数据，可以很好地估计给定模糊现象中存在的内在不确定性。本文提出了基于求解一组二次规划问题的支持向量机的高效对偶可能性回归模型。并且每个包含较少优化变量的二次规划问题使得所提出方法的训练速度很快。与其他基于支持向量机的区间回归方法相比，