Long term energy consumption forecasting using genetic programming
| dc.contributor.author | Ahmet S. YILMAZ | |
| dc.contributor.author | Ahmet Alkan | |
| dc.contributor.author | KORHAN KARABULUT | |
| dc.contributor.author | Alkan, Ahmet | |
| dc.contributor.author | Yilmaz, Ahmet S. | |
| dc.contributor.author | Karabulut, Korhan | |
| dc.date.accessioned | 2025-10-22T16:06:48Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information such as climate and previous load demand data. In this paper a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. The empirical results demonstrate successful load forecast with a low error rate. | |
| dc.identifier.citation | 1. Al-Hamadi H.M. Soliman S.A.: Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electric Power Systems Research 74 (2005) 353–361.2. Galiana F. Handschin E. Fiechter A.: Identification of stochastic electric load models from physical data. IEEE Trans. Automatic Control 19 (December (6)) (1974) 887–893.3. Sisworahardjo N.S. El-Keib A.A. Choi J. Valenzuela J. Brooks R. El-Agtal I.: A stochastic load model for an electricity market. Electric Power Systems Research 76 (2006) 500–508.4. Park J.H. Park Y.M. Lee K.Y.: Composite modeling for adaptive short term load forecasting. IEEE Trans. Power Syst. 6 (1991) 450–457.5. Charytoniuk W. Chen M.S. Van Olinda P.: Nonparametric regression based shortterm load forecasting. IEEE Trans. Power Syst. 13 (1998) 725–730.6. Zivanovic R.: Nonparametric trend model for short term electricity demand forecasting. In: Fifth International Conference on Power System Management and Control 2002 April 17–19 (2002) 347–352.7. Papadakis S.E. Theocharis J.B. Bakirtzis A.G.: A load curve based fuzzy modeling technique for short-term load forecasting. Fuzzy Sets and Systems 135 (2003) 279–303.8. Al-Kandari A.M. Soliman S.A. ElHawary M.E.: Fuzzy short term electric load forecasting. Int j of Electrical power and energy systems 26 (2004) 111-122.9. Otavio A.S. Carpinteiro A. Agnaldo J.R. Reis A. Alexandre P.A. Da Silva B.: A hierarchical neural model in short-term load forecasting. Applied Soft Computing 4 (2004) 405–412.10. Huang H.C. Hwang R.C. Hsieh J.G.: A new artificial intelligent peak power load forecaster based on non fixed neural networks. Int j of Electrical power and energy systems 24 (2002) 245-250.11. Ghiassi M. Zimbra D.K. Saidane H.: Medium term system load forecasting with a dynamic artificial neural network model. Electric Power System Research. 76 (2006) 302-316.12. Liang R.H. Cheng C.C.: Combined regression-fuzzy approach for short-term load forecasting. IEEE Proceedings-Generation Transmission and Distribution. 147 (2000) 261–266.13. Mori H. Kosemura N. Ishiguro K. Kondo T.: Short-term load forecasting with fuzzy regression tree in power systems. In: IEEE International Conference on Systems Man and Cybernetics 2001 vol. 3 October 7–10 (2001) 1948–1953.14. Koza J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection MIT Press (1992).15. Koza J.R.: Survey of genetic algorithms and genetic programming. In: Proceedings of the Wescon 95 - Conference Record: Microelectronics Communications Technology Producing Quality Products Mobile and Portable Power Emerging Technologies San Francisco CA 7.-9. November IEEE New York (1995).16. Koza J.R.: Genetic Programming Encyclopedia of Computer Science and Technology. vol. 39 (1998) 29-43. | |
| dc.identifier.doi | 10.3390/mca13020071 | |
| dc.identifier.issn | 1300-686X | |
| dc.identifier.issn | 2297-8747 | |
| dc.identifier.scopus | 2-s2.0-39549086785 | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/11277 | |
| dc.identifier.uri | https://doi.org/10.3390/mca13020071 | |
| dc.language.iso | İngilizce | |
| dc.publisher | Association for Scientific Research | |
| dc.relation.ispartof | Mathematical and Computational Applications | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | Mathematical and Computational Applications | |
| dc.subject | Matematik | |
| dc.subject | Symbolic Regression | |
| dc.subject | Genetic Programming | |
| dc.subject | Load Forecasting | |
| dc.title | Long term energy consumption forecasting using genetic programming | |
| dc.type | Article | |
| dc.type | Article | |
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| gdc.description.department | ||
| gdc.description.departmenttemp | [Karabulut K.] Department of Computer Engineering, Yasar University, 35500 Izmir, Turkey; [Alkan A.] Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey; [Yilmaz A.S.] Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey | |
| gdc.description.endpage | 80 | |
| gdc.description.issue | 2 | |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
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| gdc.oaire.keywords | genetic programming; load forecasting; symbolic regression | |
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| gdc.virtual.author | Karabulut, Korhan | |
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