LS—SVM算法在光伏短期功率预测中的应用

以光伏阵列为研究对象，分析了辐照强度、温度以及日类型对光伏阵列出力的影响。建立了光伏短期功率预测最小二乘支持向量机LS-SVM模型。依据实验数据对模型进行了验证计算．并与BP神经网络模型做了比较，其中LS-SVM模型最大相对误差值为10．54％，平均绝对百分比误差（MAPE）为8．18％，绝对误差平方和平均值的均方根（RMSE）为0．4884，表明模型预测值离散化程度较小，所有预测点均与实际值非常接近，模型具有较好的拟合效果和泛化能力．可以有效地预测短期光伏发电功率。

Application of LS-SVM Algorithm in Short-term Photovoltaic Power Prediction

The photovohaic array is used as the object of study to analyze the effect of irradiation intensity and temperature on the output of photovohaic array. The least squares support vector machine （LS-SVM） model for short-term photovohaic power prediction is established,verified and calculated on the basis of experimenlal data,and compared with BP neural network model. The maximum relative error of LS-SVM model is 10.54%, the mean absolute percentage error （MAPE） is 8.18% and the root-mean-square error （RMSE） of mean value of absolute error sum of squares is 0.4884,which means that the discretization level of predicted value of model is relatively low,all predicted points are close to their actual value and the model has a good imitative effect and generalization ability and can predict the short-term photovoltaic power effectively.