基于结合混沌纵横交叉的PSO-DBN的短期光伏功率预测

为了提高短期光伏发电预测的准确性,文中采用深度置信网络(DBN)建立了各模型函数的预测模型。通过分析各模型函数的特征,建立了光伏发电模型的功率预测。传统的基于神经网络的功率预测难以训练多层网络,影响其预测精度。DBN采用无监督贪婪逐层训练算法构建了一个在回归预测分析中具有优异性能的多隐层网络结构,已成为深度学习领域的研究热点。DBN连接权重采用结合混沌纵横交叉的粒子群优化算法(CC-PSO)优化,避免出现由随机初始化导致的局部最优解现象,从而提高了DBN网络预测性能。最后,案例测试显示了所提出模型的有效性。

Short term PV power prediction based on Particle swarm optimization combined with chaos crossover for deep belief networks

In order to improve the accuracy of short-term photovoltaic power generation forecasting,the deep belief network(DBN)is used to establish the forecasting models of each model function.The power prediction of photovoltaic power generation model is established by analyzing the characteristics of each model function.The traditional power prediction of photovoltaic model based on neural network is difficult to train multi-layer network,thus affecting its prediction accuracy.DBN uses unsupervised greedy layer-by-layer training algorithm to construct a multi-hidden layer network structure with excellent performance in regression prediction analysis,which has become a research hotspot in the field of deep learning.The weight of DBN connection is optimized by particle swarm optimization combined with chaotic crossover(CC-PSO),which avoids the phenomenon of local optimal solution caused by random initialization and improves the prediction performance of DBN network.Finally,case tests show the effectiveness of the proposed model.