1 research outputs found
Ensembling methods for countrywide short term forecasting of gas demand
Gas demand is made of three components: Residential, Industrial, and
Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each
component is studied, using Italian data as a case study. Statistical
properties and relationships with temperature are discussed, as a preliminary
step for an effective feature selection. Nine "base forecasters" are
implemented and compared: Ridge Regression, Gaussian Processes, Nearest
Neighbours, Artificial Neural Networks, Torus Model, LASSO, Elastic Net, Random
Forest, and Support Vector Regression (SVR). Based on them, four ensemble
predictors are crafted: simple average, weighted average, subset average, and
SVR aggregation. We found that ensemble predictors perform consistently better
than base ones. Moreover, our models outperformed Transmission System Operator
(TSO) predictions in a two-year out-of-sample validation. Such results suggest
that combining predictors may lead to significant performance improvements in
gas demand forecasting