Forecasting Individual Electricity Load Curves

elcf4R provides methods and supporting workflows for day-ahead forecasting of individual electricity load curves. The current package surface includes Kernel Wavelet Functional models, clustered KWF, GAM, MARS and LSTM estimators, an explicit helper to configure the Python backend used by the LSTM path, dataset adapters for iFlex, StoreNet, Low Carbon London and REFIT, scaffolded download/read support for IDEAL and GX, helpers to build daily segments, and rolling-origin benchmarking utilities.

See also

Useful links:

• https://fbertran.github.io/elcf4R/

• https://github.com/fbertran/elcf4R

• Report bugs at https://github.com/fbertran/elcf4R/issues

Author

Maintainer: Frederic Bertrand frederic.bertrand@lecnam.net (ORCID)

Authors:

• Fatima Fahs fatima.fahs@es.fr

• Myriam Maumy-Bertrand myriam.maumy@ehesp.fr (ORCID)