Intelligent short-term load forecasting in Turkey

Date

2006-09-01

Author

Topalli, Ayca Kumluca
Erkmen, İsmet
Topalli, Ihsan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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A method is proposed to forecast Turkey's total electric load one day in advance by neural networks. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to use the available data for adapting the weights and to further adjust these connections according to changing conditions. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days, weekends and special holidays. A traditional ARMA model is constructed for the same data as a benchmark. Proposed method gives lower percent errors all the time, especially for holidays. The average error for year 2002 is obtained as 1.60%.

Subject Keywords

Electrical and Electronic Engineering, Energy Engineering and Power Technology

URI

https://hdl.handle.net/11511/35209

Journal

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

DOI

https://doi.org/10.1016/j.ijepes.2006.02.004

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

A. K. Topalli, İ. Erkmen, and I. Topalli, “Intelligent short-term load forecasting in Turkey,” INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, pp. 437–447, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35209.