基于灰色模型和最小二乘支持向量机的电力短期负荷组合预测

提出一种联合灰色模型(grey model，GM)和最小二乘支持向量机回归(least square support vector regression，LSSVR)算法的电力短期负荷智能组合预测方法。在考虑负荷日周期性的基础上，通过对历史负荷数据的不同取舍，构建出各种不同的历史负荷数据序列，并对每个历史数据序列分别建立能修正b 参数的GM(1,1)灰色模型进行负荷预测；采用最小二乘支持向量机回归算法对不同灰色模型的预测结果进行非线性组合，以获取最终预测值。该方法在充分利用灰色模型所需原始数据少、建模简单、运算方便等优势的基础上，结合最小二乘支持向量机所具有的泛化能力强、非线性拟合性好、小样本等特性，提高了预测精度。仿真结果验证了所提出组合方法的有效性和实用性。

Short-Term Load Combination Forecasting by Grey Model and Least Square Support Vector Machine

A short-term load forecasting method in which the least square support vector regression (LSSVR) algorithm is intelligently combined with grey model (GM) is proposed. Considering daily periodicity of power load and by means of conditional choice of historical load data, various historical load data suites are constructed, and for each historical data suite a GM(1,1) model in which the parameter b can be modified is constructed to conduct load forecasting. By use of LSSVR, the nonlinear combination of the forecasted results by different grey models is performed to obtain final forecasting result. In the proposed forecasting method the advantages of grey model such as less raw data to be required, simple to model and convenient to calculate are fully utilized and the features of LSSVR such as strong generalization ability, good nonlinear fitting ability and less samples to be required are combined, thus the forecasting accuracy can be improved. Simulation results show that the proposed combination forecasting method is effective and practicable.