AI-based forecasting for optimised solar energy management and smart grid efficiency

What is it about?

Using AI to Predict Solar Power for Smart GridsSolar energy is a crucial piece of our transition to a cleaner future, but its production fluctuates heavily depending on the weather and time of day. This unpredictability makes it difficult for power grids to perfectly balance supply and demand. Our research uses Artificial Intelligence—specifically a Deep Learning model called Long Short-Term Memory, to accurately predict future solar electricity generation. We trained our AI to forecast solar power generation across multiple time horizons, from 15 minutes up to a week in advance. By analyzing past sunlight data, our system can accurately predict future solar irradiance, providing a smart tool for grid operators to plan ahead and keep the electricity supply stable.

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Photo by Patrick Hendry on Unsplash

Photo by Patrick Hendry on Unsplash

Why is it important?

With the urgent global push toward renewable energy and recent events highlighting the fragility of our energy infrastructure, integrating solar power reliably is more critical than ever. What makes our work unique is that we provide a highly accurate, resource-efficient forecasting tool that works seamlessly across a wide range of time horizons, consistently outperforming standard benchmark models for predictions beyond two hours. Crucially, we also openly shared our massive, cleaned dataset of over 17 million global horizontal irradiance measurements alongside our source code. This transparency, combined with the model's low computational cost once trained, means it can be practically scaled as a cloud-based machine learning service. This ultimately helps grid operators, independent solar farms, and everyday energy "prosumers" prevent power imbalances and reduce costly balancing requirements.

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