结合模糊粗糙集和支持向量机的电力负荷短期预测方法

针对支持向量机（support vector machine，SVM）负荷预测方法中存在冗余信息、数据量过大而导致的训练时间过长、速度变慢等缺陷，利用模糊粗糙集（Fuzzy Rough Sets，FRS）能有效地处理不精确或不完备知识及冗余信息的特点，提出了一种结合FRS和SVM的短期负荷预测模型，将FRS理论中的属性约简算法用于解决电力负荷中众多影响因素的信息膨胀问题，采用属性约简算法剔除与决策信息不相关的因素，将约简后的因素作为SVM的输入，并采用SVM回归算法预测短期负荷。算例仿真表明，该预测模型可保证预测精度，加快计算速度。

A Short-Term Power Load Forecasting Method Based on Fuzzy Rough Sets and Support Vector Machine

Aiming at the defects of long training time and slow speed due to redundant information and mass data in the support vector machine (SVM) based load forecasting and taking advantage of imprecise or incomplete knowledge and redundancy information handling by fuzzy rough sets (FRS), a short-term load forecasting model combining SVM and FRS is proposed, in which the attribute reduction algorithm of FRS is used to deal with the information bloat of the numerous power load affecting factors and eliminate the factors irrelevant to decision-making information; then, the simplified factors are input into SVM to conduct the forecasting. The simulation result shows that this forecasting model can ensure the prediction accuracy and speed up the calculation.