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Short Term Load Forecasting using Neuro Fuzzy


Gunjan Dave , Ahmedabad Institute of Technology, GTU ; Sweta J. Shah, IITE, Indus University; Yogesh Patel, Ahmedabad Institute of Technology, GTU ; Merolina Christie, Ahmedabad Institute of Technology, GTU ; Urvish Mewada, Ahmedabad Institute of Technology, GTU


Adaptive Neuro-Fuzzy Interface System, Load Forecasting, Short Term Load Forecasting


Optimal day to day operation of electric power generating plant is very essential for any power utility constitution to reduce input costs and the prices of electricity. So, to generate reasonably the required power, one needs to forecast the future electricity demands since power generation relies heavily on the electricity demand. Load forecast has three different horizons: short term forecast, medium term forecast and long term forecast. The Short-Term Load Forecasting (STLF) provides information for utility program system planners so that they can come up with a short-term solution to protect the transmission and distribution systems and to better serve the clients. This article presents the development of Adaptive Neuro Fuzzy Interface System (ANFIS) based short-term load forecasting model. The fusion of neural networks and fuzzy logic in neuro-fuzzy models achieves readability and learning ability at once. This article presents prediction of electric load by considering various information like time, temperature, humidity, day and historical load data. Historical load data is taken from MGVCL and weather data is taken from the website www.timeanddate.com.

Other Details

Paper ID: ETCOP001
Published in: Conference 9 : ETCO 2017
Publication Date: 01/04/2017
Page(s): 1-5

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