Optimized Clusters for Disaggregated Electricity Load Forecasting
DOI:
https://doi.org/10.57805/revstat.v8i2.92Keywords:
clustering, disaggregation, forecasting, optimization, waveletsAbstract
To account for the variation of EDF’s (the French electrical company) portfolio following the liberalization of the electrical market, it is essential to disaggregate the global load curve. 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 to optimize, a preliminary clustering of individual load curves with respect to a predictability index. The optimized clustering procedure is controlled by a forecasting performance via a cross-prediction dissimilarity index. It can be assimilated to a discrete gradient type algorithm.
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Copyright (c) 2010 REVSTAT-Statistical Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.