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Monthly Peak-load Demand Forecasting for Sulaimani Governorate Using Different Weather Conditions Based on ANN Model

Warda Hussein Ali1      Mohamed Abdullah Hussein2      Luqman Mahmood Mina3

1,2 Sulaimani Polytechnic University, Technical College of Engineering, Electrical Power Engineering Department, Sulaimani,

3 Sulaimani Polytechnic University, Technical College of Engineering, Communication Engineering Department, Sulaimani

Received: 2/12/2018,  Accepted: 6/05/2019 ,  Published: 4/2020


Mid-term forecasting of load demand is necessary for the correct operation of electric utilities. There is an on-going attention toward putting new approaches to the task. Recently, Artificial Neural Network has played a successful role in various applica-tions. This paper is presents a monthly peak-load demand forecasting for Sulaimani (located in northern Iraq) using the most widely used traditional method based on an artificial natural network, the performance of the model is tested on the actual historical monthly demand of the Governorate for the years 2013 to 2017. The standard mean absolute percentage error method is used to evaluate the accuracy of forecasting models, the results obtained shows a very good estimation of the load. The mean absolute percentage error MAPE is 0.056. 


Midterm monthly load forecast, Artificial Neural Network, Multi-Layer Perceptron (MLP), Actual load, Predicted Load. Yearly ahead 


1- H. S. Hippert, C. E. Pedreira, and R. C. Souza, "Neural Networks for Short-Term Load Forecasting: A Review and Evaluation", IEEE Trans.
On power systems, VOL. 16, NO. 1, Feb. 2001.
2- Electricity Network Development Plan Sulaimani Governorate, UNDP-ENRP, Distribution Sector Revision 1 -February 2002.
3- Anand Mohan, “  Mid Term Electrical Load Forecasting For State    of Himachal Pradesh Using Different Weather Conditions via ANN Model  ”, International Journal of Research in Management, Science & Technology , Vol. 1; No. 2, December 2013.
4- M. R. G. Al-Shakarchi and M. M. Ghulaim, "Short-Term Load Forecasting for Baghdad Electricity Region", Electric Machines and Power Systems, 28:355-371, 2000. Copyright c 2000 Taylor & Francis.
5- S.H.Ling, Frank H.F.Leung, H.K.Lam, and Peter K.S. Tam, “Short-term electric load forecasting based on a neural on a neural fuzzy network,” IEEE Trans. Ind. Electron., vol.50, no.6, Dem.2003.
6- Gwo-ching liao, Ta-peng tsao, “Integrated genetic algorithm/Tabu search and neural fuzzy networks for short-term load forecasting,” Power Engineering Society General Meeting, vol.1, pp.1082 – 1087, June 2004.
7- P.K. Dash, S.Mishra, S.Dash, A.C.Liew, “Genetic optimization of a self-organizing fuzzy-neural network for load forecasting,” IEEE 2000.
8- Electricity Sector Master Plan for Iraq, Attachment 4, Demand Forecast, USAID, JULY 2004.
9- BadarUl Islam, “Comparison of Conventional and Modern Load Forecasting Techniques Based on Artificial Intelligence and Expert Systems” IJCSI International Journal of Computer Science Issues, vol. 8, No. 3, Sep 2011, pp. 504–513.
10- Huang, G.-B., Zhu, Q.-Y., Mao., K., SIEW, C.-K., Saratchandran, P & Sundararajan, N. 2006 “Can threshold networks be trained      directly” IEE Transactions on Circuits and Systems Part 2: Express Briefs Vol.53, pp.187-191.
11- Ali Nahari, Habib Rostami, Rahman Dashti, “Electrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network” International Journal of Electronics Communication and Computer Engineering, Volume 4, Issue 6, ISSN (Online).
12- Hsu, Y.- Y., C.-C. Yang, "Design of artificial neural networks for short-term load forecasting. Part I: Self-organizing feature maps for day type selection",  lEE  Proceedings-C,  Vol.  138, No.5, September 1991,  pp. 407-413.
13- Djukanovic, M., B. Babic, D. J. Sobajic, Y.-H. Pao, "Unsupervised/supervised  learning  concept  for  24-hour  load      forecasting", lEE Proceedings-C, Vol. 140, No.4, July 1993, pp. 311-318.
14- Yong Wang, Dawu Gu, “Back Propagation Neural Network for Short-term Electricity Load Forecasting with Weather Features”, International Conference on Computational Intelligence 
     and Natural Computing (2009).
15- M.  Buhari and S.  Adamu,"  Short  Term  Load Forecasting  Using  Artificial  Neural  Network", Proceeding  of  the  International  Multi  Conference  of  Engineering  and  Computer  Scientists,  Vol.1,  p.p.221-226, March 2012.