Title :
Support vector machines (SVM) based short term electricity load-price forecasting
Author :
Swief, R.A. ; Hegazy, Y.G. ; Abdel-Salam, T.S. ; Bader, M.A.
Author_Institution :
Ain Shams Univ., Cairo, Egypt
Abstract :
This paper presents a support vector machine based combined load-price short term forecasting algorithm. The algorithm is implemented as a classifier and predictor for both load and price values. The implicit relationship between price and load is modeled employing time series. A pre-classification technique is applied to reject the unwanted data before starting the process of the data using the proposed model. In the implemented model, support vector machine plays the role of a classifier and then acts as a forecasting model. Principle component analysis (PCA) and K nearest neighbor (Knn) points techniques are applied to reduce the number of entered data entry to the model. The model has been trained, tested and validated using data from, Pennsylvania-New Jersey-Maryland. The results obtained are presented and discussed.
Keywords :
load forecasting; power engineering computing; power markets; principal component analysis; support vector machines; time series; K nearest neighbor points technique; principle component analysis; short term electricity load-price forecasting; support vector machines; Bayesian methods; Chaos; Data mining; Economic forecasting; Load forecasting; Predictive models; Support vector machine classification; Support vector machines; Wavelet analysis; Weather forecasting; Deregulation; Electricity prices; Price forecasting; Support Vector Machines (SVM); load forecasting;
Conference_Titel :
PowerTech, 2009 IEEE Bucharest
Conference_Location :
Bucharest
Print_ISBN :
978-1-4244-2234-0
Electronic_ISBN :
978-1-4244-2235-7
DOI :
10.1109/PTC.2009.5281886
