Application of nuSupport Vector Regression in Short- Term Load Forecasting
سال انتشار: 1391
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 319
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شناسه ملی سند علمی:
ICOPTICP19_041
تاریخ نمایه سازی: 26 مرداد 1397
چکیده مقاله:
Short-term load forecasting (STLF) of electric power systems plays an essential role in the optimal operation of power systems. Economic performance and reliability of a power system is substantially dependent on the load prediction. STLF is a complex process in electric grid due to having many non-linear factors, such as daily and weekly cyclical changes. Support vector regression has a good ability to estimate non-linear equations. In this paper, a new support vector machine model called nu support vector regression (nu-SVR) is proposed for electrical load forecasting. Results of the proposed method are compared with forecasting results achieved using an artificial neural network (ANN). Results show that the nu-SVR is a proper method for STLF.
کلیدواژه ها:
نویسندگان
Adnan Omidi
Faculty of Electrical and Computer Sistan and Baluchestan University, Zahadan, Iran
S Masoud Barakati
Faculty of Electrical and Computer Sistan and Baluchestan University, Zahadan, Iran
Saeed Tavakoli
Faculty of Electrical and Computer Sistan and Baluchestan University, Zahadan, Iran