基于信息融合的动态神经网络光伏功率预测

Photovoltaic power forecasting based on information fusion theory

光伏发电由于受到外界环境的制约,发电波动较大,很难保证高比率的接入,精准预测光伏功率能在提高光伏使用率的同时保障电网的安全运行和调度。在此背景下,提出一种新型光伏功率预测方法,以动态神经网络模型为构架,充分考虑各种制约光伏发电的影响因子,将其进行加权融合成一个综合影响因子λ,并用改进的共轭梯度算法对运算进行优化,针对功率波动较大的时刻,通过缩短预测时间间隔、增加隐含层层数提高预测精度。最后在岳阳临湘县民禹光伏电站进行实际验证。经验证预测方法可行有效,且精度较高。

The photovoltaic power generation depends on the uncertain external environment,therefore,the generated power has a relatively high fluctuation resulting a low ratio power access.The accurate forecasting of photovoltaic power can raise up its penetration and also keep a safe operation and dispatching of the power grid.In this paper,a prediction method for photovoltaic power is proposed based on information fusion theory.The dynamic neural network model is adopted.Then,the influence factors,which limit the photovoltaic power generation,are fully considered and a comprehensive influence factor lambda is proposed.After that,the gradient algorithm is utilized for optimization.For the period with high fluctuation,the forecast time interval is shorted and the number of layers is i ncreased to improve the prediction accuracy.Finally,the feasible and effective of the proposed theory is verified at Y ueyang photovoltaic power plant in practice.