ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Disaggregated Electricity Forecasting Using Clustering of Individual Consumers

4 September 2018, 16:20 – 16:50


Submitted by
Jairo Cugliari
Jairo Cugliari (Université de Lyon), Benajmin Auder (Université Paris Sud), Yannig Goude (Université Paris Sud / EDF R&D), Jean-Michel Poggi (Université Paris Sud, Université Paris Descartes)
We propose to build clustering tools useful for forecasting the load consumption. The idea is to disaggregate the global signal in such a way that the sum of disaggregated forecasts significantly improves the prediction of the whole global signal. The strategy is in three steps: first we cluster curves defining numerous super-consumers, then we build a hierarchy of partitions, and then the best one is finally selected with respect to a disaggregated forecast criterion.

The shape of the curves exhibits rich information about the calendar day type, the meteorological conditions or the existence of special electricity tariffs. Using the information contained in the shape of the load curves, [1] proposed a flexible nonparametric function-valued forecast model called KWF (Kernel+Wavelet+Functional) well suited to handle nonstationary series.

In [2] we applied this strategy to a dataset of individual consumers from the French electricity provider EDF. A substantial gain
of $16$ \% in forecast accuracy comparing to the 1-cluster approach is provided by disaggregation while preserving meaningful classes of consumers.

This project's aim is to evaluate the upscaling capacity of the strategy developed in [2] to cope with the up-growing volume of data. For this, we explore different strategies with simulated datasets ranging from thousands to tens of millions of consumers. Our experiments show that no sophisticated computing technology is needed to solve this problem.

A R package is under development (available in Github: github.com/cugliari/iecclust) where our strategies are implemented.

[1] A. Antoniadis, X. Brossat, J. Cugliari, and J.-M. Poggi. P\évision d'un processus à valeurs fonctionnelles en présence de non stationnarités. Application à la consommation d'électricit\é. Journal de la Société Française de Statistique, 153(2):52 -- 78, 2012.
[2] J. Cugliari, Y. Goude, and J.-M. Poggi. Disaggregated electricity forecasting using wavelet-based clustering of individual consumers. In Energy Conference, IEEE International, 2016.
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