NDLI: STLF using Neural Networks and Fuzzy for anomalous load scenarios

Content Provider	IEEE Xplore Digital Library
Author	Ahmmed, S. Khan, M.A.A. Hasan, M.K. Saber, A.Y. Huda, M.N. Rahman, M.Z.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Bangladesh (Khan, M.A.A.; Hasan, M.K.) \|\| Real-Time Power and Intelligent Systems Laboratory, Missouri University of Science and Technology, USA (Saber, A.Y.) \|\| Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, Bangladesh (Rahman, M.Z.) \|\| Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh (Ahmmed, S.; Huda, M.N.)
Abstract	Load Forecasting has an important role in load generation, scheduling, planning etc. in power system. Different computational intelligent techniques are used in Short Term Load Forecasting (STLF) to make it more effective. Neural Networks (NN) is an effective mapping algorithm that can map complex input-output relationships, which is an important technique to do STLF having existing dataset. Usually a proper NN is sufficient to achieve accepted level of performance. But different load dataset may bear some irregular nature of load demand scenario due to having special events, where accuracy of NN suffers significantly. To enhance the performance for those situations, the authors propose a hybrid STLF approach-Neural Networks and Fuzzy (NNF) method. The authors first try to select the best possible trained NN and do STLF. Considering historical data trend and of existing errors of NN solution, for those special days, NNF determine the Load Change trend. Fuzzy Inference Rules (FIR) have been developed to further improvement by fuzzy method. In fuzzy part the NNF apply FIR on two inputs: STLF of NN and Load Change trend, to enhance the performance of STLF for special events. To evaluate the proposed method it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Since the authors deal with the daily load dataset of Saudia Arabia of Hijri years (Arabian years), Hajj has been chosen as one of the anomalous load scenario. Empirical results show that for Hajj event of Hijri 1428 year, the accuracy of STLF by NN is approximately 6.37%, whereas proposed NNF can decline the error at only 1.92%.
Starting Page	722
Ending Page	725
File Size	915053
Page Count	4
File Format	PDF
ISBN	9781424462773
e-ISBN	9781424462803
DOI	10.1109/ICELCE.2010.5700794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-18
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Data structures Boolean functions Load forecasting Finite impulse response filter Forecasting Load modeling Neural networks Fuzzy systems Fuzzy inference rule Hijri years Hajj
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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