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

  • José BlancarteEmail author
  • Mireille Batton-Hubert
  • Xavier Bay
  • Marie-Agnès Girard
  • Anne Grau
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 217)


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.


Electricity Consumption Adjustment Factor Industrial Site Forecast Method Reference Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • José Blancarte
    • 1
    • 2
    Email author
  • Mireille Batton-Hubert
    • 2
  • Xavier Bay
    • 2
  • Marie-Agnès Girard
    • 2
  • Anne Grau
    • 1
  1. 1.EDF R&D, Département Eco-efficacité et Procédés IndustrielsMoret-Sur-LoingFrance
  2. 2.Ecole Nationale Supérieure des MinesSaint-EtienneFrance

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