Predicting Power Demand and Prices Using a Unified Deep Learning Model

What is it about?

This study introduces a new deep learning model, called PatchGRU, that can predict electricity demand, prices, and weather-related variables at the same time. Traditional methods often focus on just one target, such as electricity load, and ignore how these factors influence one another. PatchGRU overcomes this by learning from multiple related data sources together and predicting long sequences—up to 30 days—efficiently. The model separates stable patterns (like seasonal trends) from fast-changing ones (like sudden temperature shifts), then processes them differently for better accuracy. It uses an improved recurrent network that analyzes data in multi-scale “patches” rather than individual time points, making it faster and more stable than complex Transformer models. In experiments using real data from Australian power systems, PatchGRU consistently outperformed existing state-of-the-art forecasting models, providing more reliable results for power scheduling and renewable energy integration.

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Photo by MARIOLA GROBELSKA on Unsplash

Photo by MARIOLA GROBELSKA on Unsplash

Why is it important?

Accurate long-term electricity forecasting is critical for managing modern energy systems that increasingly depend on renewables and market-based operations. Existing models often fail to capture the complex relationships among load, price, and weather, which leads to unstable or costly decisions. The proposed PatchGRU model achieves higher accuracy with lower computational cost, enabling grid operators and energy planners to make better-informed decisions over extended periods. Its simplicity and interpretability—despite its strong performance—make it a practical tool for large-scale, real-world forecasting applications. By reducing uncertainty in energy demand and price prediction, it supports grid stability and economic efficiency in the transition toward cleaner energy systems.

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