DocumentCode :
3470826
Title :
Robust short-term load forecasting using projection statistics
Author :
Chakhchoukh, Yacine ; Panciatici, Patrick ; Bondon, Pascal ; Mili, Lamine
Author_Institution :
DMA, RTE, Versailles, France
fYear :
2009
fDate :
13-16 Dec. 2009
Firstpage :
45
Lastpage :
48
Abstract :
It has been observed that the French electric load series possesses outliers and breaks. Outliers are deviant data points while breaks are lasting abrupt changes in the stochastic pattern of the series. It turns out that outliers and breaks significantly degrade the reliability and accuracy of conventional day-ahead estimation and forecasting methods. Robust methods are needed for this application. In this paper, we propose to use a robust diagnostic approach for which the identification of outliers and breaks is carried out via a robust multivariate estimation of location and covariance based on projection statistics (PS). The developed procedure consists of the following steps: (i) estimate the parameters and the order of a high order autoregressive AR(p*) by means of the PS, (ii) execute a robust filter cleaner to identify and reject the outliers, and (iii) apply a maximum-likelihood estimator defined at the Gaussian distribution that handles missing values. The performance of this method has been evaluated on the French electric demand in terms of execution time and forecasting accuracy. This approach improves the load forecasting quality for ¿normal days¿ and presents several interesting properties such as fast execution, good robustness, simplicity and easy on-line implementation. A novel multivariate approach is also proposed in order to deal with heteroscedasticity.
Keywords :
Gaussian distribution; load forecasting; maximum likelihood estimation; statistical analysis; stochastic processes; French electric load series; Gaussian distribution; day-ahead estimation method; high order autoregressive; maximum-likelihood estimator; multivariate approach; parameter estimation; projection statistics; robust diagnostic approach; robust multivariate estimation; robust short-term load forecasting; stochastic pattern; Bonding; Conferences; Degradation; Load forecasting; Parameter estimation; Robustness; Statistics; Stochastic processes; USA Councils; Weather forecasting; Robustness; load forecasting; projection statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009 3rd IEEE International Workshop on
Conference_Location :
Aruba, Dutch Antilles
Print_ISBN :
978-1-4244-5179-1
Electronic_ISBN :
978-1-4244-5180-7
Type :
conf
DOI :
10.1109/CAMSAP.2009.5413243
Filename :
5413243
Link To Document :
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