Abstract
In the many influencing factors of load forecasting, some are related to each other, and some are independent, so it is not very necessary of all factors to the conclusions. The paper adaptively selects input features making use of Bayes method and Rough Set, and chose a group of input characteristic set which report most out the reason of output change to train the BP Neural Network model for forecasting, which has been testified to be valid.
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Li, Y. (2010). Short-Term Load Forecasting Based on Bayes and RS. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_13
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DOI: https://doi.org/10.1007/978-3-642-12990-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
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