深度置信网络光伏发电短时功率预测研究

Research on short-term power prediction of photovoltaic power generation based on deep belief network

针对现有光伏功率预测技术存在提取特征不充分导致预测精度低的问题,提出一种基于深度置信网络的光伏发电短时功率预测方法。根据光伏发电系统的运行特征和深度置信网络的特点,阐述该预测方法的可行性和科学性。搭建功率预测模型,通过无监督学习过程逐层提取输入序列的内在特征;模型顶层采用BP神经网络对特征矩阵和偏移量进行有监督训练,经过误差微调后输出预测结果。综合考虑可能对光伏发电功率产生影响的多种因素(如辐射强度、温度等),并将上述因素做归一化处理后作为模型的初始输入量,在Matlab上对预测模型进行仿真验证。最后将该预测模型与常用的BP神经网络方法进行比较,结果显示所提模型性能优于BP神经网络,证明该模型具有较好的预测准确度。

In view of the fact that the existing photovoltaic power prediction technology has insufficient extraction characteristics leading to insufficient prediction accuracy, a short-term power prediction method based on deep belief network for photovoltaic power is proposed. According to the characteristics of photovoltaic power system and the characteristics of deep belief network, the feasibility and scientificity of the prediction method are expounded. A short-term power prediction model based on depth belief network（DBN） for photovoltaic power is also proposed. Through unsupervised learning process, the model extracts the intrinsic features of the output sequence layer by layer. At the top of this model, with the help of supervised learning, the BP neural network is used to adjust the feature matrix and offsets, and the prediction results are obtained after the reset of the deviations. A variety of factors, such as radiation intensity and temperature,which may affect photovoltaic power, are comprehensively considered. And after the above factors are normalized as the initial input of the model, the prediction model is simulated and verified on the Matlab. Finally, the model is compared with the commonly used BP neural network method, and the results show that the performance of the proposed model is better than that of BP neural network, which proves that the model has good prediction accuracy.