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arXiv · 2501.09551

Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation

Nelson Salazar-Pena·Adolfo Palma-Vergara·Mateo Montes-Vera·Maria Alejandra Vargas-Torres·Rodrigo Hernandez-Vanegas·Maria Amador·Boris Rojas·Adriana Salinas·Andres Velasco·Alejandra Tabares·Andres Gonzalez-Mancera
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Abstract

The prediction of solar irradiance enhances reliability in photovoltaic (PV) solar plant generation and grid integration. In Colombia, PV plants face penalties if energy production deviates beyond governmental thresholds from intraday market offers. This research employs Long Short-Term Memory (LSTM) and Bidirectional-LSTM (Bi-LSTM) models, utilizing meteorological data from a PV plant in El Paso, Cesar, Colombia, to predict solar irradiance with a 6-hour horizon and 10-minute resolution. While Bi-LSTM showed superior performance, the LSTM model achieved comparable results with significantly reduced training time (6 hours versus 18 hours), making it computationally advantageous. The LSTM predictions were averaged to create an hourly resolution model, evaluated using Mean Absolute Error, Root-Mean-Square Error, Normalized Root-Mean-Square Error, and Mean Absolute Percentage Error metrics. Comparison with the Global Forecast System (GFS) revealed similar performance, with both models effectively capturing daily solar irradiance patterns. The forecast model integrates with an Object-Oriented power production model, enabling accurate energy offers in the intraday market while minimizing penalty costs.

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Nelson Salazar-Pena, Adolfo Palma-Vergara, Mateo Montes-Vera, Maria Alejandra Vargas-Torres, Rodrigo Hernandez-Vanegas, Maria Amador, Boris Rojas, Adriana Salinas, Andres Velasco, Alejandra Tabares, Andres Gonzalez-Mancera. 2025-01-16. Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation. https://arxiv.org/abs/2501.09551

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