jeudi 4 novembre 2010

Présentation du 19/11/10 prévision consommation électrique

Date : 19/11/10 de 12h30 à 14h00

Lieu :
19 avenue du Maine
75732 PARIS
Métro : Montparnasse, Falguière

Salle : amphi 208
Présentation par Yannig Goude (EDF R&D)

Titre : Short-term electricity load forecasting with Adaptive Generalized Additive Model

Modeling and forecasting the electricity load at short-term and middle-term horizons is a key activity for electrical companies. The need to maintain the equilibrium between the electricity supply and demand at any time is essential to avoid power systems injuries and blackouts that generate financial penalties or more important drawbacks. The French electrical load company Electricity De France (EDF) has always attached the utmost importance to that issue which stands for a central point in power system scheduling.
The advent of the wholesale electricity market in Europe and in France has brought renewed focus on load forecasting methods as the EDF demand which has been equal to the France is now submitted to customers departures or arrivals. In addition, the emergence of new consumption habits mainly due to new technologies (computers, heat pumps, flat panel displays…) entails slow modifications of the load curves.
Historical EDF models are based on parametric non-linear regression and classical time series modeling (ARIMA models) that needs a large amount of a-priori information from experts. We present a new model based on GAM methods, implemented in R thanks to the mgcv package developed by Simon Wood. This nonparametric model allows us to take into account exogenous predictors like temperature and cloud cover, as well as calendar effects (thanks to cyclic spline projection) or the lag effects of the load itself. We apply it on a part of the EDF portfolio (big customers) and show that this model can face with various situations, needs fewer a-priori information than a parametric model. To deal with the problem of non-stationnarity we propose an online update of this model, based on online recalculation of the coefficients of the projection on the spline basis. We obtain significant improvement of the forecasts, especially when parametric modeling fails.

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