Abstract
The problem of short term electric load forecasting is considered in the case when a part of input variables is given in a nonnumeric form. Novel neuro-fuzzy network architecture and learning algorithms are proposed, which enable high-rate processing of information given in different measurements scales (quantitative, ordinal, and nominal). Types and parameters of the employed membership functions may be determined by the amount of available explicit prior knowledge. Experimental comparison to a traditional neural network confirms superiority of the proposed approach.
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Bodyanskiy, Y., Popov, S., Rybalchenko, T. (2008). Multilayer Neuro-fuzzy Network for Short Term Electric Load Forecasting. In: Hirsch, E.A., Razborov, A.A., Semenov, A., Slissenko, A. (eds) Computer Science – Theory and Applications. CSR 2008. Lecture Notes in Computer Science, vol 5010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79709-8_34
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DOI: https://doi.org/10.1007/978-3-540-79709-8_34
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