PV-VLM: A Multimodal Vision-Language Approach Incorporating Sky Images for Intra-Hour Photovoltaic Power Forecasting

The rapid proliferation of solar energy has significantly expedited the integration of photovoltaic (PV) systems into contemporary power grids. Considering that the cloud dynamics frequently induce rapid fluctuations in solar irradiance, accurate intra-hour forecasting is critical for ensuring grid stability and facilitating effective energy management. To leverage complementary temporal, textual, and visual information, this paper has proposed PV-VLM, a multimodal forecasting framework that integrates temporal, textual, and visual information by three modules. The Time-Aware Module employed a PatchTST-inspired Transformer to capture both local and global dependencies in PV power time series. Meanwhile, the Prompt-Aware Module encodes textual prompts from historical statistics and dataset descriptors via a large language model. Additionally, the Vision-Aware Module utilizes a pretrained vision-language model to extract high-level semantic features from sky images, emphasizing cloud motion and irradiance fluctuations. The proposed PV-VLM is evaluated using data from a 30-kW rooftop array at Stanford University and through a transfer study on PV systems at the University of Wollongong in Australia. Comparative experiments reveal an average RMSE reduction of approximately 5% and a MAE improvement of nearly 6%, while the transfer study shows average RMSE and MAE reductions of about 7% and 9.5%, respectively. Overall, PV-VLM leverages complementary modalities to provide a robust solution for grid scheduling and energy market participation, enhancing the stability and reliability of PV integration.

A Novel Distributed PV Power Forecasting Approach Based on Time-LLM

Distributed photovoltaic (DPV) systems are essential for advancing renewable energy applications and achieving energy independence. Accurate DPV power forecasting can optimize power system planning and scheduling while significantly reducing energy loss, thus enhancing overall system efficiency and reliability. However, solar energy's intermittent nature and DPV systems' spatial distribution create significant forecasting challenges. Traditional methods often rely on costly external data, such as numerical weather prediction (NWP) and satellite images, which are difficult to scale for smaller DPV systems. To tackle this issue, this study has introduced an advanced large language model (LLM)-based time series forecasting framework Time-LLM to improve the DPV power forecasting accuracy and generalization ability. By reprogramming, the framework aligns historical power data with natural language modalities, facilitating efficient modeling of time-series data. Then Qwen2.5-3B model is integrated as the backbone LLM to process input data by leveraging its pattern recognition and inference abilities, achieving a balance between efficiency and performance. Finally, by using a flatten and linear projection layer, the LLM's high-dimensional output is transformed into the final forecasts. Experimental results indicate that Time-LLM outperforms leading recent advanced time series forecasting models, such as Transformer-based methods and MLP-based models, achieving superior accuracy in both short-term and long-term forecasting. Time-LLM also demonstrates exceptional adaptability in few-shot and zero-shot learning scenarios. To the best of the authors' knowledge, this study is the first attempt to explore the application of LLMs to DPV power forecasting, which can offer a scalable solution that eliminates reliance on costly external data sources and improve real-world forecasting accuracy.