arXiv:2306.10356v3 Announce Type: replace Abstract: Accurate forecasting of renewable generation is crucial to facilitate the integration of Renewable Energy Sources into the power system. Focusing on photovoltaic (PV) units, forecasting methods can be divided into two main categories: physics-based and data-based strategies, with Artificial Intelligence (AI)-based models providing state-of-the-art performance. However, while these AI-based models can capture complex patterns and relationships in the data, they ignore the underlying physical prior knowledge of the phenomenon. Therefore, in thi
The increasing integration of renewable energy sources into power grids necessitates more accurate forecasting methods to ensure grid stability and efficiency.
Improved forecasting of renewable generation, particularly solar, enhances grid management, reduces reliance on fossil fuel peakers, and supports the broader energy transition.
The development of more sophisticated AI models, like MATNet, allows for more precise predictions of renewable energy output by combining data-driven insights with physical understanding.
- · Renewable energy operators
- · Grid management companies
- · AI/ML developers
- · Energy consumers
- · Traditional fossil fuel generators
- · Less sophisticated forecasting model providers
Enhanced grid stability and efficiency as more renewables come online.
Accelerated adoption of solar and other intermittent renewables due to improved reliability of supply information.
Reduced need for expensive and environmentally impactful peaker plants, shifting grid infrastructure investment.
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