Abstract Accurate short-term solar irradiance forecasting is crucial for ensuring the optimum utilization of photovoltaic power generation sources. This study addresses this issue by proposing a spatiotemporal correlation model based on deep learning. The proposed model first applies a convolutional neural network (CNN) to extract spatial features from a two-dimensional matrix composed of meteorological parameters associated with a target site and its neighboring sites. Then, a long short-term memory (LSTM) network is applied to extract temporal features from historical solar irradiance time-series data associated with the target site. Finally, the spatiotemporal correlations are merged to predict global horizontal irradiance one hour in advance. The prediction performance and generalization ability of the proposed CNN-LSTM model are evaluated within a whole year, under diverse seasons and sky conditions. Three datasets are involved for case studies, which are collected from 34 locations spread across three different climate zones in Texas, USA. Moreover, the performance of the CNN-LSTM model is compared with those obtained using the CNN, LSTM, and other benchmark models based on five evaluation metrics. The results indicate that the proposed model has advantages over the other models considered and provides a good alternative for short-term solar radiation prediction.

中文翻译：

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基于具有时空相关性的混合 CNN-LSTM 模型的短期全球水平辐照度预测

摘要 准确的短期太阳辐照度预测对于确保光伏发电资源的最佳利用至关重要。本研究通过提出基于深度学习的时空相关模型来解决这个问题。所提出的模型首先应用卷积神经网络 (CNN) 从由与目标站点及其邻近站点相关的气象参数组成的二维矩阵中提取空间特征。然后，应用长短期记忆 (LSTM) 网络从与目标站点相关的历史太阳辐照度时间序列数据中提取时间特征。最后，合并时空相关性以提前一小时预测全球水平辐照度。所提出的 CNN-LSTM 模型的预测性能和泛化能力在一整年内，在不同的季节和天空条件下进行评估。案例研究涉及三个数据集，这些数据集是从分布在美国德克萨斯州三个不同气候区的 34 个地点收集的。此外，基于五个评估指标，将 CNN-LSTM 模型的性能与使用 CNN、LSTM 和其他基准模型获得的性能进行了比较。结果表明，所提出的模型比其他考虑的模型具有优势，并为短期太阳辐射预测提供了一个很好的替代方案。基于五个评估指标，将 CNN-LSTM 模型的性能与使用 CNN、LSTM 和其他基准模型获得的性能进行比较。结果表明，所提出的模型比其他考虑的模型具有优势，并为短期太阳辐射预测提供了一个很好的替代方案。基于五个评估指标，将 CNN-LSTM 模型的性能与使用 CNN、LSTM 和其他基准模型获得的性能进行比较。结果表明，所提出的模型比其他考虑的模型具有优势，并为短期太阳辐射预测提供了一个很好的替代方案。