Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case

Abstract : The estimation of baseline electricity consumptions for energy efficiency and load management measures is an essential issue. When implementing real-time energy management platforms for Automatic Monitoring and Targeting (AMT) of energy consumption, baselines shall be calculated previously and must be adaptive to sudden changes. Short Term Load Forecasting (STLF) techniques can be a solution to determine a pertinent frame of reference. In this study, two different forecasting methods are implemented and assessed: a first method based on load curve clustering and a second one based on signal decomposition using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). Both methods were applied to three different sets of data corresponding to three different industrial sites from different sectors across France. For the evaluation of the methods, a specific criterion adapted to the context of energy management is proposed. The obtained results are satisfying for both of the proposed approaches but the clustering based method shows a better performance. Perspectives for exploring different forecasting methods for these applications are considered for future works, as well as their application to different load curves from diverse industrial sectors and equipments.
Type de document :
Chapitre d'ouvrage
Modeling and Stochastic Learning for Forecasting in High Dimensions, 217, Springer, pp 1-20, 2015, Lecture Notes in Statistics, 978-3-319-18731-0. 〈10.1007/978-3-319-18732-7_1〉
Liste complète des métadonnées

https://hal-emse.ccsd.cnrs.fr/emse-01184916
Contributeur : Florent Breuil <>
Soumis le : mardi 18 août 2015 - 14:13:21
Dernière modification le : vendredi 20 octobre 2017 - 01:18:00

Identifiants

Citation

José Blancarte, Mireille Batton-Hubert, Xavier Bay, Marie-Agnès Girard, Anne Grau. Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case. Modeling and Stochastic Learning for Forecasting in High Dimensions, 217, Springer, pp 1-20, 2015, Lecture Notes in Statistics, 978-3-319-18731-0. 〈10.1007/978-3-319-18732-7_1〉. 〈emse-01184916〉

Partager

Métriques

Consultations de la notice

104