Abstract:

Accurate prediction of photovoltaic（PV） power generation is of great significance for improving stability of power system, ensuring power quality, and optimizing grid operation. In order to solve the problems of low precision and poor performance of the existing PV prediction algorithms, and to utilize the ability of Multi-Layer Perceptron（MLP） to solve nonlinear problems and the advantages of Deep Belief Networks（DBN） to effectively process large amounts of complex data, a PV prediction algorithm combining MLP and DBN（MLP-DBN） is constructed. The basic idea is to make preliminary predictions using MLP model, then input the residuals between observed values and the predicted values into DBN model to forecast, finally modify MLP’s predictions by residual’s predictions. Using the simulation data of PV power generation, model’s predictive performance under different learning rates is explored, and the parameters are searched for optimal settings. Using performance indexes such as root mean square error, average absolute error, and determination coefficient, compared with traditional prediction algorithm Support Vector Machine（SVM） and deep learning algorithm Long Short-Term Memory（LSTM） with higher prediction accuracy, the simulation results show that the performance of MLP-DBN algorithm has been significantly improved, which can provide a high-precision and high-performance algorithm for PV power and effectively solve the problem of PV power prediction.

Key words: photovoltaic generation prediction, deep learning, Support Vector Machine（SVM）, Long Short-Term Memory（LSTM）, Multi-Layer Perceptron-Deep Belief Networks（MLP-DBN） algorithm

摘要：

精确的光伏发电预测对提高电力系统稳定性、保证电能质量、优化电网运行具有重大意义。为了解决现存光伏预测算法精度较低、性能较差的问题，同时为了综合利用多层感知器（MLP）解决非线性问题的能力以及深度信念网络（DBN）有效处理大量复杂数据的优势，构建了一种融合MLP和DBN的光伏预测算法（MLP-DBN），其基本思想是先利用MLP模型进行初步预测，再将观测值与预测值的残差输入DBN预测模型进行预测，最后用残差预测值对MLP模型的预测值进行修正。利用光伏发电实测数据仿真，探究了不同学习率下模型的预测性能，并对模型的各参数进行了寻找优化设置。使用均方根误差、平均绝对误差以及决定系数等性能指标评估结果表明，与传统的预测算法支持向量机（SVM）以及具有较高预测精度的深度学习算法长短期记忆网络（LSTM）相比，MLP-DBN算法性能有明显的提升，为光伏发电提供了一种高精度高性能的预测算法，可以有效解决光伏发电预测问题。

关键词: 光伏发电预测, 深度学习, 支持向量机（SVM）, 长短期记忆网络（LSTM）, 多层感知器-深度信念网络（MLP-DBN）算法