Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting
Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection for 2009-2011. Actual historical regional data which consists of economic, electricity statistic and weather during 1995-2008 are applied as inputs. The networks structure is firstly justified using true historical data of 1995-2005 to forecast peak load of 2006-2008. Afterwards, peak load forecasting of 2009-2011 is conducted subsequently using actual historical data of 1995-2008. Overall, the proposed networks shown better performance compared to that obtained by Levenberg-Marquardt-Feedforward network, Double-log Multiple Regression, and with projection by PLN for 2006-2010.
Tanoto Y, Ongsakul W, Marpaung COP. Long-term Peak Load Forecasting Using LM-Feedforward Neural Network for Java-Madura-Bali Interconnection, Indonesia. PEA-AIT International Conference on Energy and Sustainable Development: Issues and Strategies. Bangkok. 2010: 1-6.
Kuncoro AH, Zuhal, and Dalimi R. Longterm Peak Load Forecasting on the Java-Madura- Bali Electricity System Using Artificial Neural Network Method. International Conference on Advances in Nuclear Science and Engineering in Conjunction with LKSTN. Bandung. 2007: 177-181.
Kermanshahi B. Recurrent Neural Network for Forecasting Next 10 Years Loads of Nine Japanese Utilities. Neurocomputing. 1998; 23: 125-133.
Kermanshahi B, Iwamiya H. Up to Year 2020 Load Forecasting Using Neural Nets. Electrical Power and Energy System. 2002; 24: 789-797.
El-Ela AA. El-Zeftawy AA, Allam SM, Atta GM. Long-term Load Forecasting and Economical Operation of Wind Farms for Egyptian Electrical Network. Electric Power Systems Research. 2009; 79: 1032-1037.
Parlos AG, Patton AD. Long-term Electric Load Forecasting Using Dynamic Neural Network Architecture. Joint International Power Conference Athens Power Tech. Athena. 1993; Volume 2: 816-820.
Lina R, Yanxin L, Zhiyuan R, Haiyan L, Ruicheng F. Application of Elman Neural Network and MATLAB to Load Forecasting. International Conference on Information Technology and Computer Science. Washington DC. 2009; Volume 1: 55-59.
Hagan MT, Menhaj MB. Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks. 1994; 5(6): 989-993.
Ghods L, Kalantar M. Methods for Long-term Electric Load Demand Forecasting: A Comprehensive Investigation. IEEE International Conference on Industrial Technology. Chengdu. 2008: 1-4.
Daneshi, H. et al. Long-term load forecasting in electricity market. IEEE International Conference on Electro/Information Technology. Ames. 2008 Page(s):395 – 400.
Tanoto Y. Long-term Peak Load Forecasting Using Artificial Neural Networks: The Case of Java-Madura-Bali Interconnection, Indonesia. Master Thesis. Bangkok, Asian Institute of Technology; 2010.
Nguyen D, Widrow B. Improving The Learning Speed of 2-Layer Neural Networks by Choosing Initial Values of The Adaptive Weights. The International Joint Conference on Neural Networks. San Diego.1990; Volume 3: 21-26.
Ferdinando H, Pasila F, Kuswanto H. Enhanced Neuro Fuzzy Architecture for Electrical Load Forecasting. TELKOMNIKA: Indonesian Journal of Electrical Engineering. 2010; 8(2): 87-96.
Jadid MN, Fairbairn DR. Predicting Moment-Curvature Parameters from Experimental Data. Engineering Application Artifical Intelligence.1996; 9(3): 303-319.
PT. PLN (Persero). National Electricity Planning and Provision (RUPTL 2006-2015–in Bahasa Indonesia). Jakarta. 2006.
PT. PLN (Persero). 2009. National Electricity Planning and Provision (RUPTL 2009-2018–in Bahasa Indonesia). Jakarta. 2009.
Article MetricsAbstract view : 127 times
PDF - 125 times
- There are currently no refbacks.
Copyright (c) 2014 Universitas Ahmad Dahlan
TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604
View TELKOMNIKA Stats
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.