# Ensembling methods for countrywide short term forecasting of gas demand

## TL;DR

This study compares various forecasting models for short-term gas demand prediction in Italy, demonstrating that ensemble methods outperform individual models and existing TSO predictions, with potential for improved accuracy.

## Contribution

It introduces and evaluates ensemble forecasting methods for gas demand, showing their superiority over individual models and current industry predictions.

## Key findings

- Ensemble predictors outperform base models in accuracy.
- Models outperform Transmission System Operator (TSO) predictions.
- Ensemble methods show consistent improvements in out-of-sample validation.

## Abstract

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.