Forecasting models for a daily load curve using ANFIS (Adaptive Neuro-Fuzzy Inference System) data such as power, current, winding temperature, oil temperature and atmospheric temperature etc. After training networks using actual historical load and data properly processed, results indicate that ANFIS forecasting model presented clear superiority with features of simple algorithm, high accuracy and high stability and is more adaptable to the applications in design of load forecasting at substation transformer. The proposed method of a electrical load forecasting with forecasted load management with the work presents a methodology for estimating the maximum power that can be extracted from distribution substation transformers based on Estimated values of future load, current temperature values measured at various locations within the transformer, and transformer reliability requirements.

1. J. Yasuoka, J. L. P. Brittes, L. C. Msgrini, H. P. Schmidt, 1. A. Jardini.“ANN-based Real-Time Short-Term Load Forecasting in Distribution Substations”, IEEE/PES T&D 2002 Latin Americu, 2002, Siio Paulo.2002.
2. Joe Perez, ERL Phase Power Technologies, Winnipeg, Fundamental Principles of Transformer Thermal Loading and ProtectionWestern protective relay conference October 2010.
3. Facilities Engineering Branch Denver Office Denver, Colorado “Permissible Loading Of Oil-Immersed Transformers And Regulators” Internet Version Of This Manual Created September 2000
4. G. Gavrilovs, O. Borscevskis “Replacement of Power Transformers basis onDiagnostic Results and Load Forecasting” World Academy of Science, Engineering and Technology 76 2011.
5. Andrew P. Douglas Arthur M. Breipahl Fred N. Lee Rambabu Adapa “Risk Due to Load Forecast Uncertainty in Short Term Power System Planning”
6. G. W. Swift, S. E. Zocholl, M. Bajpai, J. F. Burger, C. H. Castro, S. R. Chano, F. Cobelo, P. De Sá, E. C. Fennell,J. G. Gilbert, S. E. Grier, R. W. Haas, W. G. Hartmann, R. A. Hedding, P. Kerrigan, S. Mazumdar, D. H. Miller,P. G. Mysore, M. Nagpal, R. V. Rebbapragada
7. Jose A. Jardini, Fellow, IEEE, Jose L. P. Bnttes, Member, IEEE, Luiz C. Magrini, Jorge Yasuoka, Student Member, IEEE, Paula S. D. Kayano, Josue de Camargo, Hemin P. Schmidt, Member, IEEE “Management of Available Power in Substation Transformers Based on Reliability” 2004 IEEEPES Transmission a Distribution Conference & Exposition:LatinAmerica
8. Stochastic Modelling Compared With Artificial Intelligence Based Approach For Electrical Load Forecasting A. Seif E. M. Gabr ,M. A. Moustafa Hassan,O. Y. Abul-Haggag Journal of American Science, 2011;7(4)
9. Neural and Mathematical Modeling Approaches for Hourly Long Term Load Forecasting Ümmühan Ba?aran Filik, Ömer Nezih Gerek, Mehmet Kurban ICIC Express Letters International ? 2009 ISSN 1881-803X.
10. “Short-Range Load Forecasting for Distribution System Planning -- An Improved Method for Extrapolating Feeder Load Growth” H. N. Tram, Senior Member, IEEE H. L. Willis, Senior Member, IEEE G. B. Rackliffe, Member, IEE. AB6 Advanced Systems Technology 777 Penn Center Blvd. Pittsburgh, PA 15235.
11. “ANN-based Real-Time Short-Term Load Forecasting in Distribution Substations” Hernán P. Schmidt, PhD, Jorge Yasuoka, BSc, José Luiz P. Brittes, MSc, Luiz C. Magrini, PhD, and José A. Jardini, Fellow, IEEE.
12. „Short-Term Load Forecasting Using Soft Computing Techniques? D. K. Chaturvedi, Sinha Anand Premdayal, Ashish Chandiok. Int. J. Communications, Network and System Sciences, 2010, 3, 273-279
13. Risk Assessment for Transformer Loading Weihui Fu, Student Member, IEEE, James D. McCalley, Senior Member, IEEE, and Vijay Vittal, Fellow, IEEE IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 3, AUGUST 2001
14. Evaluation of Permissible Loading for Indoor Oil-Immersed Distribution Transformers Ali Mamizadeh, and res skender
15. Loading guide for oil-immersed power transformers, IEC 354, 1991.
16. High-Voltage/Low-Voltage Prefabricated Substations, IEC 61330:1995, 199
17. XIA, Chang Hao , LIU, Yong, LI, Xian Shan China Three Gorges University „Neural Network Models and Design of Virtual Instrument Based on Joint Programming of LabVIEW and MATLAB for Short-term Load Forecasting of Electric Power System”

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