Electricity load forecasting with LSTM

Demo project for electricity load forecasting with a LSTM (abbr. "Long Term Short Term Memory", a Recurrent Neural Network) with data for Switzerland.

Getting started

It is recommended to use a Python and R packages and environment management tool like Anaconda. This will install also already most of the packages used in this project and will allow to run the notebooks in ./notebooks/ with the Jupyter Notebook server.

Main packages used

* [Keras 2.0.2](https://github.com/fchollet/keras) - High-level Neural Network API used for LSTM modelling. Backend used was TensorFlow.
* [forecast 8.0](https://cran.r-project.org/web/packages/forecast/index.html) - Forecasting package for R used for TBATS and ARIMA benchmark forecasts. 

Data:

• Hourly series for actual load and forecasted load. Source: ENTSO-E API
• Hourly series for temperature (in °F) and a mapping of the qualitative weather in one of the 3 categories defined in the configuration file. These historical observations are collected for the major cities in Switzerland. Source: Dark Sky API
• Calendar dummy variables. Holidays, month, day of week, hour of day.
• In total: 20756x78 observations

File structure:

.
├── data                        # Load, calendar and weather data
├── lstm_load_forecasting       # Helper functions for data preparation and LSTM model building
├── models                      # All trained models saved in HDF5 file format
├── notebooks                   # LSTM Model selection and forecast comparison
├── results                     # Results and parameters from model training run comparison
├── config.json                 # Config file with API Keys, Weather stations, holiday calendar.  
├── LICENSE
└── README.md

Notebooks

Different "categories" (named data modules in the data helper functions...) of models have been defined and then for each category, a number of models based on possible parameter combinations have been estimated:

• Model 1: Using only the ENTSO-E forecast as input.
• Model 2: Using only the calendar dummy variables as input.
• Model 3: Using only the weather data as input.
• Model 4: Using ENTSO-E and calendar data as input.
• Model 5: Using weather data and calendar dummies as input.
• Model 6: Using ENTSO-E, calendar and weather as input.

The LSTM model results notebook then combines all results. The best models models in category 4, 5 and 6 then are compared in the Forecast Comparison notebook with a TBATS and a ARIMA model forecasts that have been generated in the respective R notebooks. The comparison then is further extended with rolling window (static, using at each time step the newly available data) in the Rolling Forecast notebook.

Acknowledgments and References

• Marino et al. (2016)
• http://www.jakob-aungiers.com/articles/a/LSTM-Neural-Network-for-Time-Series-Prediction