摘要
针对最小二乘支持向量机(LSSVM)中参数选取对电力负荷预测精度有着较大的影响,建立了一种基于人工免疫算法优化最小二乘支持向量机的短期电力负荷预测模型,该模型以历史负荷数据作为输入向量,选用高斯径向基函数作为核函数,利用人工免疫算法对LSSVM中的惩罚因子和核参数进行优化选取,极大地提高了LSSVM的训练速度和预测精度。仿真结果表明,该方法在短期电力负荷预测中具有较高的预测精度,证实了该方法的有效性和可行性。
Considering the fact that the parameter selection for least squares support vector machine （LSSVM） has a large impact on the power load forecasting accuracy, a model for short-term power load forecasting model based on least squares support vector machine of artificial immune algorithm was established, in which the historical load datas are used as input vectors, and the Gaussian radial basis function is used as kernel function. In the model, artificial immune algorithm is used to select LSSVM penalty factor and nuclear parameters, which greatly improves LSSVM training speed and forecasting accuracy. Simulation results show that the method has higher prediction accuracy in short-term power load forecasting and confirms the effectiveness and feasibility of the method.
出处
《山西电力》
2014年第2期4-7,共4页
Shanxi Electric Power
关键词
人工免疫算法
最小二乘支持向量机
短期电力负荷预测
参数优化
artificial immune algorithm
least squares support vector machine
short-term power load forecasting
parameteroptimization
