Since PV forecasting depends heavily on weather predictions, inaccuracies in meteorological models can impact power output estimates. Researchers from the Institute of Statistics at the Karlsruhe Institute of Technology investigated ways to improve forecast precision through post-processing techniques. Their study evaluated three methods: adjusting weather forecasts before inputting them into PV models, refining solar power predictions after processing, and leveraging machine learning to predict solar power directly from weather data.
"Weather forecasts aren't perfect, and those errors get carried into solar power predictions," explained Nina Horat, lead author of the study. "By tweaking the forecasts at different stages, we can significantly improve how well we predict solar energy production."
The study found that applying post-processing techniques to power predictions, rather than weather forecasts, yielded the most significant improvements. While machine learning models generally outperformed conventional statistical methods, their advantage was marginal in this case, likely due to the constraints of the available input data. Researchers also highlighted the importance of including time-of-day information in models to enhance forecast accuracy.
"One of our biggest takeaways was just how important the time of day is," said Sebastian Lerch, corresponding author of the study. "We saw major improvements when we trained separate models for each hour of the day or fed time directly into the algorithms."
A particularly promising approach involves bypassing traditional PV models altogether by using machine learning algorithms to predict solar power directly from weather data. This technique eliminates the need for detailed knowledge of a solar plant's configuration, relying instead on historical weather and performance data for training.
The findings pave the way for further advancements in machine learning-based forecasting, including the integration of additional weather variables and the application of these methods across multiple solar installations. As renewable energy adoption accelerates, improving solar power forecasting will be key to maintaining grid stability and efficiency.
Research Report:Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
Related Links
Institute of Atmosphere at CAS
All About Solar Energy at SolarDaily.com
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