Authors:José Blancarte, Valérie Murin and Mireille Batton-Hubert, Xavier Bay,
Demand response has attracted much attention in recent years. It has been identified as one of the solutions to overcome electricity network equilibrium problems related to peak consumptions and renewables intermittency. From all the different consumption sectors, industrial electricity consumptions are of particular interest due to the magnitude they represent. Thus, industrial consumptions (equipments, workshops, etc.) can be considered as resources for demand response programs to be integrated in balancing the electricity network. In order to be able to integrate these consumptions, it is necessary to standardize the procedures to evaluate their availability as well as forecasting their behavior. The standardization of the procedures shall focus on the program constraints in which the resource is valued, but most importantly, in ensuring the safety of the electricity network. This research paper focuses in three industrial usages with different behaviors. For this purpose, three different forecasting techniques, adapted to the data and operational constraints, are used. Specific performance indicators called “trust” parameters are proposed and calculated for different times of the day for the different usages. The different forecasting techniques show similar performances depending mainly on the data. The estimated trust parameters allow evaluating the probability for a specific consumption of not complying with the operational constraints.
Keywords: Demand Response, Energy Management, Industry, Short-Term Load Forecasting, Load Curve Clustering, Lasso Regression