Modeling and Forecasting Short-Term Electricity Demand for Libyan Electric Network
الشريط الجانبي للمقالة
محتوى المقالة الرئيسي
الملخص
Electrical load forecasting is a method of guessing the future of the electricity demand. Load forecast is an essential factor for power system planners and demand controllers. This is to ensure that would be an adequate amount of the electricity meeting the increasing demands. Therefore, an accurate load forecasting model can effectively lead to reduction of power system cost, better budget planning, and maintenance scheduling. The future load on a power system is predicted by extrapolating a predetermined relationship between the load and its influential variables. Determination of this relationship involves modeling and estimation the coefficient of the model through the use of an efficient parameter estimation technique. This paper presents a short term forecasting model for Libyan electric network using multi parameter regression method. The proposed method shows a result with small deviation between the actual and expected load, which means Multi parameter regression emerged as a suitable model for forecasting electricity demand in Libya.
تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
المراجع
M. P. D. Thakare, “Short Term Load Forecasting by Using Data Mining Techniques,” International Journal of Science and Research (IJSR), 2012.
M. Markou, E. Kyriakides, and M. Polycarpou “24-Hour Ahead Short Term Load Forecasting Using Multiple MLP,” University of Cyprus.
Eugene A. Feinberg, and Dora Genethliou “Applied Mathematics for power system,” State University of New York, Stony Brook, pp 269-285.
Salah H. E. Saleh, and A. N. Mansur, “Forecasting of the Electricity Demand in Libya Using Time Series Stochastic Method for Long -Term From 2011-2022,” International Journal of Innovative Research in Science, Engineering and Technology, 2014.
V. Agarwal, A. Bougaev, and L. Tsoukalas. “Kernel Regression Based Short-Term Load Forecasting,” in Proc. 2006 16th International Conference on Artificial Neural Networks, pp. 701-708.
O. Hyde, and P. F. Hodnett, “An adaptable automated procedure for short-term electricity load forecasting”, IEEE Trans. Power Systems ,vol. 12, no. 1, pp. 84-94, 1997.
S. Huang, and K. Shih, “Short-term load forecasting via ARM model identification including non-Gaussian process considerations,” IEEE Trans. Power Systems, vol. 18, no. 2, pp. 673-679, 2003.
G. Juberias, R. Yunta, J. Garcia Moreno, and C. Mendivil, “A new ARIMA model for hourly load forecasting,” in Proc. 1999 IEEE Transmission and Distribution Conf., vol. 1, pp. 314-319.
A. Rewagad, and V. Soanawane, “Artificial neural network based short term load forecasting,” in Proc. 1998 IEEE Region 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control, Vol. 2, pp. 588-595.
P. J. Santos, A. G. Martins, and A. J. Pires, “Short-term load forecasting based on ANN applied to electrical distribution substations,” in Proc. 2004 39th International Universities Power Engineering Conf., Vol. 1, pp. 427-432.
Z. Gontar, N. Hatziargyriou, “Short term load forecasting with radial basis function network,” in Proc. 2001 IEEE Porto Power Tech Conf., Vol. 3, pp. 4, 10-13. 6
Z. Gontar, G. Sideratos, N. Hatziargyriou, “Short-Term Load Forecasting Using Radial Basis Function Networks,” in Methods and Applications of Artificial Intelligence, Springer-Verlag, Vol. 3025/2004, pp. 432-438, 2004.
Z. Bao, D. Pi, and Y. Sun, “Short-Term Load Forecasting Based on Self-organizing Map and Support Vector Machine,” in Advances in Natural Computation, vol. 3610/2005, pp. 688-691, Springer- Verlag, 2005.
Z. M. Shamseldin, “Short Term Electrical Load Forecasting Using Fuzzy Logic”, Sudan University, Sudan, 2015.
B. Ye, C. Guo, and Y. Cao, “Short-term load forecasting using a new fuzzy modeling strategy,” in Proc. 2004 5th World Congress on Intelligent Control and Automation, pp. 5045-5049.
B. Ye, N. Yan, C. Guo, and Y. Cao, “Identification of fuzzy model for short-term load forecasting using evolutionar programming and orthogonal least squares,” in Proc. 2006 IEEE Power Engineering Society General Meeting.
Z. Hua, and Z. Lizi, “The factor analysis of short-term load forecast based on wavelet transform,” in Proc. 2002 International Conference on Power System Technology, Vol. 2, pp. 1073-1076.
M. Khamaira, “Electrical Load Forecasting”, MSc. Thesis, Academe of graduate studies, Tripoli, Libya, 2007.
Mohammad R. Altwir, “long-term load forecasting by using End-use methods to predict Libya electrical network,” Tripoli University, Libya, Tripoli, 2012.
Salah H. Saleh, and A. N. Mansur, “Forecasting of the Electricity Demand in Libya Using Time Series Stochastic Method for Long -Term From 2011-2022,” International Journal of Innovative Research in Science, Engineering and Technology, 2014.
M. U. Fahad, and N. Arbab, “Factor Affecting Short Term Load Forecasting”, Journal of Clean Energy Technologies, Vol. 2, No. 4, October 2014.
Girraj Singh and D.S. Chauhan, “Factor Affecting Elements and Short term Load forecasting Based on Multiple Linear Regression Method,” International Journal of Engineering Research & Technology (IJERT), 2014.
National Control Center of the General Electricity Company Of Libya (GECOL), Libya, Tripoli
www.wunderground.com