Title :
Evaluation of cosine radial basis function neural networks on electric power load forecasting
Author :
Karayiannis, Nicolaos B. ; Balasubramanian, Mahesh ; Malki, Heidar A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Houston Univ., TX, USA
Abstract :
This paper presents the results of a study aimed at the development of a system for short-term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This comparison indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs on the testing data.
Keywords :
learning (artificial intelligence); load forecasting; power system analysis computing; radial basis function networks; cosine radial basis function neural networks; electric power load forecasting; feedforward neural networks; learning algorithms; power demand; power load data; training data; Clustering algorithms; Feedforward neural networks; Function approximation; Load forecasting; Neural networks; Power engineering and energy; Predictive models; Radial basis function networks; Testing; Weather forecasting;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223732
