基于支撑向量机的电力系统峰负荷预测

Support Vector Machine Approach for Peak Load Forecasting

将支撑向量机(SVM)方法用于电力系统峰负荷预测,它具有精度高、全局最优等显著特点.为了确定SVM中直接影响其推广能力的超参数,与一般采用的试凑法不同,提出了利用交叉有效性验证方法确定这些参数.另外,在样本的输入信息中,除负荷变量外,还根据峰负荷预测的特点,加入了对峰负荷预测影响较大的温度变量、星期类型及节假日信息,以提高预测精度.实际算例表明,在相同的负荷及气象数据的前提下,该方法的预测精度比神经网络方法提高了0.4%～0.8%.

A new algorithm with high forecasting accuracy and global optimal property for peak load forecasting is proposed based on the support vector machine (SVM) method, where the cross-validation is introduced into hyper-parameter estimation in SVM to outperform the common cut and try method. In addition to the load variables, the temperature information, weekday and vacation information are taken into account in the input samples to improve the forecasting accuracy. The practical examples show that the accuracy of the SVM is 0.4%-0.8% higher than artificial neural network under the same load and weather conditions.