Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting

Benjamin Auder, Jairo Cugliari, Yannig Goude and Jean-Michel Poggi
Additional contact information
Benjamin Auder: LMO, University Paris-Sud, 91405 Orsay, France
Jairo Cugliari: ERIC EA 3083, University de Lyon, Lyon 2, 69676 Bron, France
Yannig Goude: EDF R & D, LMO, Univ Paris-Sud, 91405 Orsay, France
Jean-Michel Poggi: University Paris Descartes & LMO, Univ. Paris-Sud, 91405 Orsay, France

Energies, 2018, vol. 11, issue 7, 1-22

Abstract: Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R—the free software environment for statistical computing—so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available.

Keywords: clustering; forecasting; hierarchical time-series; individual electrical consumers; scalable; short term; smart meters; wavelets (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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