Authors: Andrea Marziali; Emanuele Fabbiani; Giuseppe De Nicolao
Addresses: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy ' Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy ' Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
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. [Received: June 30, 2019; Accepted: September 29, 2019]
Keywords: natural gas; time series forecasting; neural networks; statistical learning; ensemble methods.
International Journal of Oil, Gas and Coal Technology, 2021 Vol.26 No.2, pp.184 - 201
Received: 30 Jun 2019
Accepted: 29 Sep 2019
Published online: 22 Jan 2021 *