Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866929752220106752 |
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| author | Salazar-Pena, Nelson Palma-Vergara, Adolfo Montes-Vera, Mateo Vargas-Torres, Maria Alejandra Hernandez-Vanegas, Rodrigo Amador, Maria Rojas, Boris Salinas, Adriana Velasco, Andres Tabares, Alejandra Gonzalez-Mancera, Andres |
| author_facet | Salazar-Pena, Nelson Palma-Vergara, Adolfo Montes-Vera, Mateo Vargas-Torres, Maria Alejandra Hernandez-Vanegas, Rodrigo Amador, Maria Rojas, Boris Salinas, Adriana Velasco, Andres Tabares, Alejandra Gonzalez-Mancera, Andres |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09551 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation Salazar-Pena, Nelson Palma-Vergara, Adolfo Montes-Vera, Mateo Vargas-Torres, Maria Alejandra Hernandez-Vanegas, Rodrigo Amador, Maria Rojas, Boris Salinas, Adriana Velasco, Andres Tabares, Alejandra Gonzalez-Mancera, Andres Machine Learning Systems and Control Signal Processing 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. |
| title | Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation |
| topic | Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2501.09551 |