新能源功率预测算法优化研究

The Optimization Research Approaches for Renewable Energy Output Forecasting

以风能和太阳能为代表的新能源具有随机性、间歇性和波动性,对新能源发电功率进行预测是有效解决以上问题的途径。在确定性预测中充分考虑风电出力和预测模型特性,提出分段支持向量机(piecewisesupport vector machine,PSVM)和神经网络(neural network,NN)预测算法;充分考虑天气特征对光伏出力的影响,提出基于气象特性分析的光伏出力预测算法。通过若干风电场的算例分析,证明了上述几种预测模型的实用性,为功率预测的可靠性分析提供支持。

Randomness,intermittence and fluctuation are features of new energy,which includes wind energy and solar energy,and power forecasting is an effective solution.The characteristics of wind power output and forecasting model are fully considered to propose piecewise support vector machine(PSVM)and neural network(NN)model;the effort of weather condition on photovoltaic is analyzed to optimize the forecasting model.The case studies from several wind farms and photovoltaic power stations prove that the proposed models have higher precision,which offer support for reliability analysis of power output forecasting.