Monthly Maximum load Demand Forecasting for Sulaimani Governorate Using Different Weather Conditions Based on Artificial Neural Network Model
DOI:
https://doi.org/10.21928/uhdjst.v4n2y2020.pp10-17Keywords:
Midterm monthly load forecast, Artificial Neural Network, Multi-Layer Perceptron (MLP), Actual load, Predicted Load, Yearly aheadAbstract
Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to 1 year ahead. It suits outage and maintenance planning, as well as load switching operation. There is an on-going attention toward putting new approaches to the task. Recently, artificial neural network has played a successful role in various applications. This paper is presents a monthly peak load demand forecasting for Sulaimani (located in North 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 2014–2018. The standard mean absolute percentage error (MAPE) method is used to evaluate the accuracy of forecasting models, the results obtained show a very good estimation of the load. The MAPE is 0.056.
References
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[2] United Nations Development Programme. “Electricity Network Development Plan Sulaimani Governorate, UNDP-ENRP, Distribution Sector Revision 1 February”. United Nations Development Programme, New York. 2002.
[3] A. 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 and Technology, vol. 1, no. 2, ???, 2013.
[4] M. R. G. Al-Shakarchi and M. M. Ghulaim. “Short-term load forecasting for baghdad electricity region. Electric Machines and Power Systems, vol. 28, pp. 355-371, 2000.
[5] S. H. Ling, F. H. F. Leung, H. K. Lam and P. K. S. Tam. “Short-term Electric Load Forecasting Based on a Neural on a Neural Fuzzy Network”. Vol. 50. In: IEEE Transactions on Industrial Electronics, 2003.
[6] G. C. Liao and T. P. 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, 2004.
[7] P. K. Dash, S. Mishra, S. Dash, A. C. Liew. “Genetic Optimization of a Self-organizing Fuzzy-Neural Network for Load Forecasting”. In: IEEE Power Engineering Society Winter Meeting, Conference Proceedings, 2000.
[8] United States Agency for International Development. Electricity Sector Master Plan for Iraq. United States Agency for International Development, Washington, DC, United States, 2004.
[9] B. 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, pp. 504-513, 2011.
[10] G. B. Huang, Q. Y. Zhu, K. Mao, C. K. Siew, P. Saratchandran and N. Sundararajan. “Can threshold networks be trained directly”. Vol.53. In: IEE Transactions on Circuits and Systems Part 2: Express Briefs, pp. 187-191, 2006.
[11] A. Nahari, H. Rostami, R. Dashti. “Electrical load forecasting in power distribution network by using artificial neural network”. International Journal of Electronics Communication and Computer Engineering, vol. 4, no. 6, 2013.
[12] Y. Y. Hsu and C. C. Yang. “Design of Artificial Neural Networks for Short-term Load Forecasting. Part I: Self-organizing Feature Maps for Day Type Selection”. Vol. 138. In: IEEE Proceedings-C, pp. 407-413, 1991.
[13] M. Djukanovic, B. Babic, D. J. Sobajic and Y. H. Pao. “Unsupervised/Supervised Learning Concept for 24-Hour Load Forecasting”. Vol.140. In: IEE Proceedings-C, pp. 311-318, 1993.
[14] Y. Wang and D. Gu. “Back Propagation Neural Network for Short-term Electricity Load Forecasting with Weather Features”. In: International Conference on Computational Intelligence and Natural Computing, 2009.
[15] M. Buhari and S. Adamu. “Short Term Load Forecasting Using Artificial Neural Naetwork”. Vol. 1. In: Proceeding of the International Multi Conference of Engineering and Computer Scientists, pp.221-226, 2012.
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Published
2020-07-05
How to Cite
Abdulkareem, N. H. (2020). Monthly Maximum load Demand Forecasting for Sulaimani Governorate Using Different Weather Conditions Based on Artificial Neural Network Model. UHD Journal of Science and Technology, 4(2), 10–17. https://doi.org/10.21928/uhdjst.v4n2y2020.pp10-17
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