Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation
This work addresses penalty minimization for PV plants in Colombia's intraday market, but it is incremental as it applies existing methods to a specific regional context.
This research tackled the problem of predicting solar irradiance for photovoltaic plants in Colombia to avoid penalties from intraday market deviations by using LSTM and Bi-LSTM models, achieving comparable performance to the Global Forecast System with reduced training time (6 hours vs. 18 hours for LSTM).
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.