基于深度信念网络的光伏电站短期发电量预测

为了解决现有光伏电站短期发电量预测方法存在的预测模型复杂、预测误差较大、泛化能力较低的问题,提出一种基于深度信念网络的短期发电量预测方法。首先综合考虑影响光伏出力的环境因素和光伏板的运行参数以及光伏电站历史发电量数据,对深度信念网络进行训练和学习。在此基础上,采用重构误差的方法确定深度信念网络隐含层层数。最后针对某光伏电站短期发电量进行预测算例分析,验证了该预测模型能主动选择样本抽象特征、自动确定隐含层层数,对短期发电量预测精度较高。对比前馈反向传播(Back Propagation, BP)神经网络预测模型与长短期记忆网络(Long/Short Term Memory, LSTM)预测模型,结果表明所提方法运算量低、预测精度高,且增加神经网络的深度比改进神经网络神经元对预测效果更有效。

Short-term power generation forecast of PV power station based on deep belief network

In order to solve the problems of complex prediction model, large prediction error and low generalization ability in the existing short-term power generation prediction methods, a short-term power generation prediction method based on deep belief network is proposed. Firstly, the environmental factors affecting PV output, the operation parameters of PV panels and the historical power generation data of PV power stations are comprehensively considered to train and learn the deep belief network. Then, the hidden layers of deep belief network are determined by reconstruction error. Finally, a case study of a photovoltaic power station''s short-term power generation is carried out to verify that the prediction model can actively select the abstract characteristics of samples and automatically determine the hidden layers, and has a high prediction accuracy for short-term power generation. Comparing the Back Propagation (BP) neural network prediction model and the Long/Short Term Memory (LSTM) prediction model, the results show that the proposed method has low computational cost and high prediction accuracy, and that increasing the depth of the neural network is more effective than improving the neural network neurons for the prediction effect. This work is supported by National Natural Science Foundation of China (No. 51977133).