# Forecasting residential gas demand: machine learning approaches and seasonal role of temperature forecasts

## TL;DR

This study compares machine learning models for one-day-ahead residential gas demand forecasting in Italy, emphasizing the impact of temperature forecast errors and introducing a model to quantify this effect.

## Contribution

It introduces a novel model to quantify how temperature forecast errors propagate into gas demand prediction errors, enhancing forecasting accuracy assessment.

## Key findings

- ANN achieved the lowest RMSE among models.
- GP was the best in terms of MAE.
- Temperature forecast errors contribute approximately 18% to the gas demand forecast error.

## Abstract

Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neighbour, Artificial Neural Network (ANN), and Torus Model. Italian demand data from 2007 to 2017 are used for training and testing the proposed algorithms. The choice of the relevant covariates and the most significant aspects of the pre-processing and feature extraction steps are discussed in-depth, lending particular attention to the role of one-day-ahead temperature forecasts. Our best model, in terms of Root Mean Squared Error (RMSE), is the ANN, closely followed by the GP. If the Mean Absolute Error (MAE) is taken as an error measure, the GP becomes the best model, although by a narrow margin. A main novel contribution is the development of a model describing the propagation of temperature errors to gas forecasting errors that is successfully validated on experimental data. Being able to predict the quantitative impact of temperature forecasts on gas forecasts could be useful in order to assess potential improvement margins associated with more sophisticated weather forecasts. On the Italian data, it is shown that temperature forecast errors account for some 18% of the mean squared error of gas demand forecasts provided by ANN.