基于PSO-DBN神经网络的光伏短期发电出力预测

考虑到光伏输出功率的随机性和波动性,提出一种基于粒子群算法(Particle Swarm Optimization,PSO)优化深度信念网络(Deep Belief Network,DBN)的光伏短期发电出力预测方法。首先利用改进粒子群算法确定DBN神经网络最优的初始权值,建立初始DBN网络。其次,确定预测日后,利用灰色关联度法选出与预测日气象特征相似度高的日期。将这些日期的气象数据和历史发电数据作为训练集对初始DBN网络进行训练,建立预测模型。最后仿真结果表明,所用模型相比于传统的DBN神经网络具有更高的预测精度。

Short-term power generation output prediction based on a PSO-DBN neural network

Considering the randomness and volatility of photovoltaic output power, a short-term power generation output prediction method is proposed based on optimizing a Deep Belief Network (DBN) with Particle Swarm Optimization (PSO). First, the PSO algorithm is used to determine the optimal initial weight of the DBN neural network, and the initial DBN network is established. Second, after the prediction date is determined, the grey correlation degree method is used to select the date with high similarity to the predicted daily meteorological features. Meteorological data and historical power generation data are used as training sets to train the initial DBN network and establish a prediction model. Finally, the simulation results show that the model used in this paper has higher prediction accuracy than the traditional DBN neural network. This work is supported by National Natural Science Foundation of China (No. 51477070).