Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30372
Iraqi Short Term Electrical Load Forecasting Based On Interval Type-2 Fuzzy Logic

Authors: Firas M. Tuaimah, Huda M. Abdul Abbas

Abstract:

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity.

Keywords: short term load forecasting, prediction interval, type 2 fuzzy logic systems

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337373

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1549

References:


[1] Agus Dharma, Imam Robandi and Mauridhi Hery Purnomo, "Application of Interval Type-2 Fuzzy Logic System in Short Term Load Forecasting on Special Days” IPTEK, The Journal for Technology and Science, Vol. 22, No. 2, May 2011.
[2] Abbas Khosravi, Saeid Nahavandi, Doug Creighton, and Dipti Srinivasan, "Interval Type-2 Fuzzy Logic Systems for Load Forecasting: A Comparative Study”, IEEE Trans.on power system, Vol. 27, NO. 3, august 2012.
[3] Abbas Khosravi, Saeid Nahavandi and Doug Creighton, "Short Term Load Forecasting Using Interval Type-2 Fuzzy Logic Systems”, IEEE International Conference on Fuzzy Systems, June, 2011.
[4] Agus Dharma, Imam Robandi and Mauridhi Hery Purnomo, "Application of Short Term Load Forecasting on Special Days Using Interval Type-2 Fuzzy Inference Systems: Study Case in Bali Indonesia”, Journal of Theoretical and Applied Information Technology, Vol. 49 No.2, 20th March 2013.
[5] Jerry M. Mendel, Robert I. John and Feilong Liu, "Interval Type-2 Fuzzy Logic Systems Made Simple”, IEEE Trans. On Fuzzy Systems, Vol. 14, No. 6, 2006.
[6] Qilian Liang and Jerry M. Mendel, "Interval Type-2 Fuzzy Logic Systems: Theory and Design”, IEEE Trans. On Fuzzy Systems, Vol. 8, No. 5, 2000.
[7] Oscar Castillo and Patricia Melin, "Type-2 Fuzzy Logic: Theory and Applications”, Studies in Fuzziness and Soft Computing, Vol 223, Springer-Verlag Berlin Heidelberg, 2008.
[8] Farah N, Khadir M.T., Bouaziz I. and Kennouche, H. "Short-term Forecasting of Algerian Load Using Fuzzy Logic And Expert System”, IEEE, 2009.
[9] Thiang and Yongky Kurniawan, "Electrical Load Time Series Data Forecasting Using Interval Type-2 Fuzzy Logic System”, IEEE, 2010.