Title :
Short-term load forecasting using an artificial neural network
Author :
Shimakura, Y. ; Fujisawa, Y. ; Maeda, Y. ; Makino, R. ; Kishi, Y. ; Ono, M. ; Fann, J.-Y. ; Fukusima, N.
Author_Institution :
Hokuriku Electr. Power Co., Toyama, Japan
Abstract :
This paper discusses an artificial neural network (ANN) model for short-term load forecasting. A two-step training method to cope with a shortage of training data and overfitting problems is proposed. A limit is conducted to the range where the ANN´s weights are allowed to change in order to preserve the general relation between the inputs and the output of the ANN. The ANN trained with this two-step training method demonstrates improved accuracy over conventional methods, including ANNs which employ ordinary training algorithms.
Keywords :
learning (artificial intelligence); load forecasting; neural nets; power systems; accuracy; artificial neural network; overfitting problems; power systems; short-term load forecasting; two-step training method; weights; Artificial neural networks; Data mining; Linear regression; Load forecasting; Load modeling; Predictive models; Statistical analysis; Stress; Training data; Weather forecasting;
Conference_Titel :
Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of
Conference_Location :
Yokohama, Japan
Print_ISBN :
0-7803-1217-1
DOI :
10.1109/ANN.1993.264285
