基于时间序列的深度学习光伏发电模型研究

为了减少光伏系统接入电网产生的不利影响,并对预测光伏功率输出进行研究,提出了一种基于数据中潜在季节类别的混合深度学习模型。整个模型分为三个阶段,即聚类、训练和预测。在聚类阶段,采用相关分析和自组织映射来选择历史数据中相关性最高的因素。在训练阶段,将卷积神经网络、长短期记忆神经网络和注意力机制相结合,以构建用于预测的混合深度学习模型。在预测阶段,按测试集的月份选择分类的预测模型。实验结果表明,该实验方法在7.5 min内的间隔预测中具有较高的准确性。

Deep learning photovoltaic power generation model based on time series

In order to stabilize the process which integrates photovoltaic (PV) power into a power grid, a hybrid deep learning model based on potential season category in the data is proposed after researching the prediction of PV output power. The overall model is divided into three stages, namely, clustering, training and prediction. In the clustering stage, correlation analysis and self-organizing mapping are employed to select the features with the highest correlation in historical data. In the training stage, the CNN, LSTM and the attention mechanism are combined to construct a hybrid deep learning forecasting model. In the prediction stage, a particular classification model is selected based on the month of the testing dataset. The experimental result shows that this proposed model has significantly improved prediction accuracy in terms of a time interval of 7.5 min.This work is supported by the Basic Public Welfare Research Project of Zhejiang Province (No. LGF18F020017).