Application of multi-source data fusion on intelligent prediction of photovoltaic power

Abstract

Accurate photovoltaic power prediction is crucial for reducing the uncertainty of photovoltaic systems in grid operation. Variations in cloud cover are one of the primary factors leading to fluctuations in photovoltaic power generation. However, clouds possess highly complex three-dimensional structures. Existing photovoltaic power prediction methods typically rely on two-dimensional cloud images, which are insufficient for fully capturing the impact of clouds on photovoltaic power generation. To address these challenges, this paper proposes a Multi-source data Photovoltaic power Prediction Model (MPPM) based on satellite cloud images and meteorological data. MPPM mainly includes SpatioTemporal feature Conditional Diffusion Model (STCDM), Attention Stacked LSTM network (ASLSTM) and Multidimensional Feature Fusion Module (MFFM). STCDM model utilizes a diffusion model to accurately forecast two-dimensional satellite cloud imagery. ASLSTM extracts the features of three-dimensional meteorological elements. MFFM module integrates the two-dimensional satellite cloud imagery features with the three-dimensional meteorological element features. The two-dimensional satellite cloud imagery reflects the visible aspect of cloud layers, while the three-dimensional meteorological data, which includes cloud water and ice content correlated with altitude, mainly captures the invisible aspect of cloud layers. These two sets of information complement each other, enabling a more comprehensive capture of the three-dimensional characteristics of clouds. Satellite cloud image prediction experiment and photovoltaic power prediction experiment are carried out on STCDM and MPPM model respectively. The experimental results demonstrate that the STCDM model achieved a Structural Similarity index (SSIM) of 0.909 for predicting satellite cloud imagery within 1 h and 0.789 within 24 h. Meanwhile, the MPPM model attained a pearson Correlation coefficient (CORR) of 0.945 for predicting PV power within 1 h and 0.856 within 24 h. These findings indicate that both the STCDM and MPPM models outperform other comparative algorithms in forecasting satellite cloud imagery and PV power.