Levenberg-Marquardt Recurrent Networks for Long- Term Electricity Peak Load Forecasting

Tanoto, Yusak and Ongsakul, Weerakorn and Marpaung, Charles O.P. (2011) Levenberg-Marquardt Recurrent Networks for Long- Term Electricity Peak Load Forecasting. TELKOMNIKA, 9 (2). pp. 257-266. ISSN 1693-6930

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    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.

    Item Type: Article
    Uncontrolled Keywords: Elman and Jordan recurrent neural network, long-term peak load forecasting, Levenberg-Marquardt algorithm
    Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Faculty of Industrial Technology > Electrical Engineering Department
    Depositing User: Admin
    Date Deposited: 05 Nov 2012 18:10
    Last Modified: 05 Nov 2012 18:10
    URI: http://repository.petra.ac.id/id/eprint/15840

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