Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting
Abstract: 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.
Keywords: Elman and Jordan
recurrent neural network, long-term peak load forecasting, LevenbergMarquardt
algorithm
Author: Yusak Tanoto,
Weerakorn Ongsakul, Charles O.P. Marpaung
Journal Code: jptkomputergg110033
