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.