General approach
Energy forecasting is about creating mathematical models to predict future energy supply and demand.
Input data sources
The input data sources used in the energy forecasting models are:- Target time series itself
- Weather forecasts
- Other exogenous information
Approaches and frameworks
Any Python framework or code can be used in the Rebase Platform to develop energy forecasting models. Several commonly used frameworks already have pre-implemented energy forecasting models. Below is a list of frameworks that include an implemented model:Model | Developer | Model type | Code and docs |
---|---|---|---|
sklearn.LinearRegression | Scikit-learn | Linear regression | Code, Docs |
statsmodels.ARIMA | Statsmodels | ARIMA | Code, Docs |
sklearn.RandomForestRegressor | Scikit-learn | Random forest regression | Code, Docs |
sklearn.RandomForestRegressor | Scikit-learn | Random forest regression | Code, Docs |
LightGBM | Microsoft | Gradient boosting decision trees | Code, Docs |
XGBoost | DMLC | Gradient boosting decision trees | Code, Docs |
tensorflow | Neural networks | Code, Docs |