Short term load forecasting using fuzzy neural network modified by the similarity and subsethood measures

Article type: Research Article

Authors: Ganjavi, M.R. | Lucas, C.^; | Javidi, M.H.

Affiliations: Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; and Ghods–Niroo Consulting Engineers Co., P.O. Box 15745-516, Tehran, Iran. E-mail: [email protected] | Department of Electrical & Computer Engineering, Tehran University, Tehran, Iran; and Intelligent Systems Research Faculty, P.O. Box 19395-5746, Tehran, Iran. E-mail: [email protected] | Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. E-mail: [email protected]

Note: [] Corresponding author.

Abstract: In this paper the linguistic approximate reasoning method is used for short term load forecasting. A neural structure for inference processing units is put forward. Two different — Analogical and Deductive — approaches to the inference method have been distinguished. Correspondingly, two different architectures — Analogical and Deductive fuzzy neural networks — are introduced in this paper. The accuracy of the load forecasting, as well as the size of the required rule base have been compared for Analogical, Deductive, and conventional fuzzy neural networks. It is shown that Analogical and Deductive approaches have superior performance in this application.

Journal: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 347-357, 1999