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Title: :  SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY
PaperId: :  C-1400
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 1 Issue 5 2016
DUI:    16.0415/IJARIIE-C-1400
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Auther NameAuther Institute
Kuldeep SJain University, Bangalore
Dr. Anitha GSR V College of Engineering, Bangalore.

Abstract

Electrical & Electronics Engineering
load forecasting, time series, regression, neural networks, support vector
Load forecasting is a very important function in electrical power systems. Accurate load forecasting is the need for economic cost saving. Short-Term Load Forecasting (STLF) is one of the forecasting methods that have a time frame of a few hours to about a day. STLF is required for adequate scheduling and operation of power systems. For the past several decades, there have been various methods used in load forecasting. Being a highly required activity both from a power system and economic point of view, extensive research is being made in the area of load forecasting. Some of the methods proposed earlier, and still being used are Box-Jenkins models, Auto Regressive Integrated Moving Average (ARIMA) models, Kalman filtering models, and the spectral expansion techniques-based models. Recent techniques are based on Artificial Intelligence methods like artificial neural networks (ANN), fuzzy logic, expert systems, support vector methods (SVM). Each technique has its own advantages and shortcomings. This paper does a comparative study of the methods with their positives and limitations.

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IJARIIE Kuldeep S, and Dr. Anitha GS. "SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY" International Journal Of Advance Research And Innovative Ideas In Education Volume 1 Issue 5 2016 Page 31-37
MLA Kuldeep S, and Dr. Anitha GS. "SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY." International Journal Of Advance Research And Innovative Ideas In Education 1.5(2016) : 31-37.
APA Kuldeep S, & Dr. Anitha GS. (2016). SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY. International Journal Of Advance Research And Innovative Ideas In Education, 1(5), 31-37.
Chicago Kuldeep S, and Dr. Anitha GS. "SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY." International Journal Of Advance Research And Innovative Ideas In Education 1, no. 5 (2016) : 31-37.
Oxford Kuldeep S, and Dr. Anitha GS. 'SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY', International Journal Of Advance Research And Innovative Ideas In Education, vol. 1, no. 5, 2016, p. 31-37. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/SHORT_TERM_LOAD_FORECASTING_METHODS__A_COMPARATIVE_STUDY_1400.pdf (Accessed : ).
Harvard Kuldeep S, and Dr. Anitha GS. (2016) 'SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY', International Journal Of Advance Research And Innovative Ideas In Education, 1(5), pp. 31-37IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/SHORT_TERM_LOAD_FORECASTING_METHODS__A_COMPARATIVE_STUDY_1400.pdf (Accessed : )
IEEE Kuldeep S, and Dr. Anitha GS, "SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY," International Journal Of Advance Research And Innovative Ideas In Education, vol. 1, no. 5, pp. 31-37, 9 2016. [Online]. Available: https://ijariie.com/AdminUploadPdf/SHORT_TERM_LOAD_FORECASTING_METHODS__A_COMPARATIVE_STUDY_1400.pdf [Accessed : ].
Turabian Kuldeep S, and Dr. Anitha GS. "SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 1 number 5 ().
Vancouver Kuldeep S, and Dr. Anitha GS. SHORT TERM LOAD FORECASTING METHODS, A COMPARATIVE STUDY. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2016 [Cited : ]; 1(5) : 31-37. Available from: https://ijariie.com/AdminUploadPdf/SHORT_TERM_LOAD_FORECASTING_METHODS__A_COMPARATIVE_STUDY_1400.pdf
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