A novel approach for short-term load forecasting using support vector machines.
A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.
Tian
Liang
L
Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6109, USA. tian@csee.wvu.edu
Noore
Afzel
A
D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013997:Time Factors