Certificate of Completion
THIS ACKNOWLEDGES THAT
HAS COMPLETED THE FALL 2023 DATA SCIENCE BOOT CAMP
Roman Holowinsky, PhD
DECEMBER 07, 2023
Rouzbeh Modarresi Yazdi, Nicolas Fortier, Souparna Purohit, Irem Altiner
Day-ahead price forecasting for New York City energy markets using standard time-series features (prices at previous time steps) as well as some exogenous data (natural gas prices in the area, weather-related features, etc). We trained and compared ARIMA, Dense NN, Convolutional NN, LSTMs and XGBoost regressors against a reasonable baseline model. These models could eventually be expanded and used by industrial entities to determine whether current Day-Ahead prices are advantageous compared to expected Real-Time prices 24 hours in the future.