Abstract
This work presents the development of a novel hybrid system called Hierarchical Neuro-Fuzzy BSP (HNFB) and its application in electric load forecasting. The HNFB system is based on the BSP partitioning (Binary Space Partitioning) of the input space and has been developed in order to bypass the traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure. To test the HNFB system, we have used monthly load data of six electric energy companies. The results are compared with other forecast methods, such as Neural Networks and Box & Jenkins.
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de Souza, F.J., Vellasco, M.M.R., Pacheco, M.A.C. (2001). The Hierarchical Neuro-Fuzzy BSP Model: An Application in Electric Load Forecasting. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_21
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DOI: https://doi.org/10.1007/3-540-45720-8_21
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