基于改进权值优化模型的光伏功率区间预测

区间预测方法可以反映光伏发电功率可能的变化范围，提供比点预测方法更丰富的预测信息。文章提出了一种基于径向基函数（radial basis function，RBF）神经网络的区间预测模型以直接输出光伏功率预测区间。为优化模型输出区间的性能和避免惩罚系数选择问题，构建了一种考虑区间预测偏差信息的改进预测区间优化模型，并采用粒子群算法（particle swarm optimization，PSO）求解，获得最优RBF神经网络输出权值以提高预测区间的可信度和准确性。通过对比传统区间优化模型和所提改进区间优化模型的预测结果，发现改进区间优化模型能够获得宽度更窄和预测偏差更小的光伏功率预测区间，可为调度决策提供更准确的辅助信息。

Interval Prediction of Photovoltaic Power Applying Improved Weight Optimization Model

Interval prediction method can reflect the possible range of photovoltaic power， and provide more abundant forecasting information than point prediction method. An interval prediction model based on Radial Basis Function （RBF） neural network is proposed to directly output the prediction interval of photovoltaic power. In order to optimize the performance of the output interval of the model and avoid the problem of choosing penalty coefficient， an improved prediction interval optimization model considering the bias information of the interval prediction is constructed and solved by particle swarm optimization （PSO） algorithm to obtain the optimal RBF neural network output weights， and improve the reliability and accuracy of the prediction interval. By comparing the prediction results of the traditional interval optimization model and the improved interval optimization model， it is found that the improved interval optimization model can obtain more accurate prediction interval of photovoltaic power， and can provide more accurate auxiliary information for dispatching decision-making.