一种基于改进灰色BP神经网络组合的光伏出力预测方法

光伏发电具有典型的间歇性、波动性等特点。准确预测光伏出力对电网调度、电网规划、提升新能源发电竞争力具有重要意义。提出了一种基于改进灰色BP神经网络的多模型组合光伏出力预测方法,采用常规GM(1,1)模型、幂函数变换GM(1,1)模型、基于残差修正的GM(1,1)模型以及等维新息GM(1,1)四种模型,利用BP神经网络对光伏出力的单一灰色预测结果进行优化组合输出,并根据输出值和期望值的偏差自动调整组合权值。该方法通过将多个单一预测结果组合成样本训练BP神经网络来获得较优权系数,避免了数值求解权系数的复杂过程,能够得到更为精确的预测结果。采用湖北某地光伏系统实际出力数据对该预测方法进行了验证。计算结果表明该基于改进灰色BP神经网络组合的光伏出力预测方法能够明显提高光伏出力预测精度。

A prediction method of PV output power based on the combination of improved grey back propagation neural network

Photovoltaic (PV) power generation has the typical characteristics of intermittence and volatility. Therefore, it is of great importance to accurately predict solar output for optimization of power grid scheduling, power grid planning, and improving the competitiveness of the renewable energy power generation. Based on the modified grey back propagation (BP) neural network, this paper proposes a multi-model combination photovoltaic output power prediction method. The conventional grey model, the power function transformation grey model, the residual modification grey model and the equal-dimension-newly-information grey model are used and all single grey forecasting results are optimized combination by utilizing BP neural network. The combination weights are automatically adjusted according to the deviation of the output values and expected values. This method avoids the complex process of calculating weight coefficient. By integrating multiple single prediction results as the sample to train the BP neural network, it can finally obtain the optimal weights and accurate prediction results. The forecasting is realized based on real PV data of Hubei power grid. Example calculation demonstrates that the proposed method can significantly improve the prediction accuracy of photovoltaic output. This work is supported by National Natural Science Foundation of China (No. 51277135 and No. 50707021).