Energy Demand Prediction
This workflow forecasts hourly energy demand by aligning hourly timestamps, generating lag features and training an LSTM deep neural network. It includes:
Data ingestion of historical energy consumption data
Timestamp alignment, missing value handling, and generation of lagged features as predictors
Training and application of a Keras-based LSTM deep neural network to forecast energy demand per hour
Make sure to select the proper Conda environment for Keras under "Preferences > Python Deep Learning". For more info and installation guidance, check the pertinent docs.
Comparison of actual vs. predicted values via line plots and scoring metrics.