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The processed data contains hourly day-ahead energy prices, wind forecasts, actual wind, load forecasts, actual loads, and real-time prices. These data sets were used to build an ensemble of machine learning models and the EnergyBidMaster App, which forecasts Day-Ahead and Real-Time Energy Prices within 24 hours of the forecast date to inform market bidding decisions, utilizing data available at forecast runtime. 

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The software EnergyBidMaster for intelligent bidding in energy markets can be publicly accessed from  https://energybidmaster.com/

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Published study:

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Ibebuchi, C.C. (2025). Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors. Forecasting, 7, 18. https://doi.org/10.3390/forecast7020018

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