## 1 Introduction
To enhance the understanding of the temporal correlation in renewable energy, the GAN's generator incorporated LSTM units [23]. Numerous articles [24; 25; 26; 27; 28; 29] have focused on predicting renewable energy, highlighting the benefits of Big Data analysis and sophisticated feature extraction. Methods based on deep learning techniques are particularly effective in exploring the attributes of higher dimensional data, bypassing the need for complex pre-existing knowledge. However, the above methods predict PV power scenarios based only on the historical PV power data of the target PV station, and the coupling relationship between target site and neighboring sites is ignored, which may miss valid representative scenarios of PV power. To address the aforementioned issue, this paper proposes a data-driven framework that considers spatial and temporal information from a large number of neighboring sites to develop a short-term photovoltaic power prediction model for the target site. By introducing the convolutional long short-term memory with attention (Conv-LSTM-ATT) model algorithm, which combines the convolutional long short-term memory (Conv-LSTM) module with the attention mechanism, the model adaptively allocates different levels of attention to the photovoltaic power time series at different time points, allowing it to focus on crucial time series and improve prediction accuracy. The main contributions of this paper are listed below. 1. High-Precision Short-Term Photovoltaic Power Prediction Method: This method integrates numerical weather prediction (NWP) data from the target photovoltaic station and highly correlated features from surrounding photovoltaic stations using a deep learning model, significantly enhancing the safety of grid operations. 2. Calculation and Application of Composite Similarity Index: This study first analyzes the correlation between irradiance and power sequences and their relationship with distance factors, calculates a composite similarity index between the target site and other regional photovoltaic stations, and selects data sources based on similarity, providing more precise data input for model training. 3. Application of Bayesian Optimization Techniques: Optimal data fusion ratios are determined through Bayesian optimization techniques, effectively balancing exploration and exploitation, enhancing the model's predictive accuracy and stability. 4. Development and Application of the Conv-LSTM-ATT Model: A hybrid deep learning model combining convolutional long short-term memory (Conv-LSTM) with the attention mechanism (ATT) is developed, which better handles the spatiotemporal features in time series data, improving the accuracy of crucial time series predictions. 5. Experimental Validation: Tests on real-world datasets validate the superiority of the proposed model over three other classical models in short-term photovoltaic power prediction. The structure of this paper is outlined as follows. In Section 1, we provide an overview of the related work in the field. In Section 2, we present the problem formulations. Our novel deep learning approach for PV power prediction is introduced in Section 3. A real-world dataset is used for conducting experiments in Section 4, where we compare the prediction performance with several existing methods. Finally, in Section 5, we conclude the paper.