Four methods for short-term load forecasting using the benefits of artificial intelligence

Date

2003-09-01

Author

Erkmen, İsmet
Topalli, AK

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Four methods are developed for short-term load forecasting and are tested with the actual data from the Turkish Electrical Authority. The method giving the most successful forecasts is a hybrid neural network model which combines off-line and on-line learning and performs real-time forecasts 24-hours in advance. Loads from all day types are predicted with 1.7273% average error for working days, 1.7506% for Saturdays and 2.0605% for Sundays.

Subject Keywords

General Engineering, Electrical and Electronic Engineering, Applied Mathematics

URI

https://hdl.handle.net/11511/48893

Journal

ELECTRICAL ENGINEERING

DOI

https://doi.org/10.1007/s00202-003-0163-9

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

İ. Erkmen and A. Topalli, “Four methods for short-term load forecasting using the benefits of artificial intelligence,” ELECTRICAL ENGINEERING, pp. 229–233, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48893.