Enhancing Price Forecasting Accuracy in Reserve Markets with a focus on addressing the challenges in forecasting FCR-N prices for BSPs using data from Fingrid’s open data platform

This thesis investigates machine learning techniques to improve forecasting accuracy for Fre quency Containment Reserve for Normal operation (FCR-N) prices in reserve markets, with a focus on Finland’s market data obtained from Fingrid’s open data platform. Given the impor tance of accurate price predictions for Balancing Service Providers (BSPs), the study evaluates multiple forecasting models, including regression-based, gradient boosting, and neural net work approaches, to address the complexities in reserve pricing. Key features, such as non solar and non-wind generation and FCR-N price lags, were incorporated as predictors, while solar and windforecasts were excluded due totheir low correlation with FCR-N prices. Results indicate that models like LightGBM and recurrent neural networks (RNNs) demonstrate high predictiveaccuracy, capturingtemporaltrendseffectively. However,limitations,includingdata granularity and modelinterpretability, suggest areas for future work, such as integrating exter nal market factors and improving real-time adaptability. This study underscores the potential of machine learning to enhance price forecasting in reserve markets, aiding BSPs in strate gic decision-making and supporting more robust energy management in the evolving energy landscape.