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AI Research
arXiv CS.AI·May 21·5 min read
Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting
TL;DR
The intermittent nature of solar power generation creates challenges for grid stability, necessitating improved forecasting models. This study developed a multi-horizon forecasting approach using deep learning techniques to predict photovoltaic (PV) power output.
✦ Why It Matters
Engineers can implement multi-horizon forecasting to improve solar power integration and grid reliability.
Key Takeaways
- Explore multi-horizon forecasting for solar power predictions.
- Utilize deep learning models to enhance forecasting accuracy.
- Incorporate ground-based sky images in forecasting methods.
- Reduce prediction error to improve grid stability.
Full Summary
As solar photovoltaic (PV) capacity continues to grow, reaching 597 GW in 2024, accurate forecasting of power output becomes critical for maintaining grid stability. This research introduces a multi-horizon forecasting method that leverages deep learning techniques to predict PV power output over various time frames.
The approach utilizes ground-based sky images (GSI) to capture the dynamic nature of solar irradiance. By evaluating multiple architectures, the study identifies the most effective models for long-term predictions.
Results indicate a significant improvement in forecasting accuracy, with metrics showing a reduction in prediction error by up to 20%. These findings suggest that adopting advanced forecasting methods can enhance energy management and integration of solar power into the grid.