Abstract
Load forecasting allows electric utilities to enhance energy purchasing and generation, load switching, contracts negotiation and infrastructure development [1].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Iyer, V., Che, C., Gedeon, T.: A Fuzzy-Neural Approach to Electricity Load and Spot Price Forecasting. Tencom (2003)
Guo, X., Chen, Z., Ge, H., Liang, Y.: Short-Term Load Forecasting Using Neural Network With Principal Components Analysis. In: 3rd International Conference on Machine Learning and Cybernetics, Shanghai, China (2004)
Tao, X., Renmu, H., Peng, W., Dongjie, X.: Input Dimension Reduction for Load Forecasting Based on Support Vector Machines. In: 2004 IEEE Conference on Electric Utility Deregulation, Restructuring and Power Technologies (2004)
Oliveira, C.M.: Modelo Adaptativo Para Previsão De Carga Ativa De Curto Prazo. PhD Thesis, Production Eng. Dept. – UFSC (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferro, H.F., Wazlawick, R.S., de Oliveira, C.M., Bastos, R.C. (2007). A Method to Optimize the Parameter Selection in Short Term Load Forecasting. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-74565-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74564-8
Online ISBN: 978-3-540-74565-5
eBook Packages: Computer ScienceComputer Science (R0)