Abstract
This paper presents Neural Network solutions for 24 hours ahead electrical load forecasting of normal working days in Belgium. Our approach introduces several models, each one predicting a different characteristic load point of the day. The differences in behaviour between these load points led us to such differentiation as opposed to homogeneous load curve forecasting. Results compared to the human experts' forecasts indicate the neural networks perform adequately.
Preview
Unable to display preview. Download preview PDF.
References
Monteyne M., Dongier F., de Viron F., Doulliez P., Claus J., “Neural network prototype for short-term load forecasting in Belgium”. ICANN95, Paris, France, October 1995.
Niebur D. et al “Artificial neural networks for power systems-A literature survey”. CIGRE TF38-06-06, Engineering Intelligent Systems, Vol. 1, N° 3, December 1993.
Y. Chauvin and D.E. Rumelhart, Eds. “Backpropagation: Theory, Architectures, and Applications”, Hove UK: Lawrence Erlbaum Associates Publishers, 1995, pp. 6.
A.S. Weigend, B.A. Huberman and D.E. Rumelhart, “Predicting the future A connectionist approach”, International Journal of Neural Systems, Vol. 1, No. 3, pp. 193–209, 1990.
A.Piras, A. Germond, Y. Jaccard, B. Buchenel, “Field test experiences with neural network models for short term electrical load forecasting in WestSwitzerland”, E tijdschrift, Vol. 112, No. 2–3, pp. 38–41, December 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Monteyne, M. et al. (1997). Different model types for short-term forecasting of characteristic load points. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020285
Download citation
DOI: https://doi.org/10.1007/BFb0020285
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63631-1
Online ISBN: 978-3-540-69620-9
eBook Packages: Springer Book Archive