Abstract
This paper introduces a methodology to forecast electricity consumption. A Multi-Version System (MVS) methodology combines different data mining methods to compensate weaknesses of each individual method and to improve the overall performance of the system. The current benchmark forecasting system in use is a Regression system, which is in need of improvement after the structural as well as operational changes in the electricity supply markets. Experiments are Modelled on the Regression and the prototype Neural Network system. The results indicate that, in some cases, the Neural Network Model has performed better than the Regression System.
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© 2003 Springer-Verlag Berlin Heidelberg
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Panesar, S.S., Wang, W. (2003). Electricity Demand Forecasting Using Neural Networks. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_114
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DOI: https://doi.org/10.1007/978-3-540-45080-1_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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