A Comparative Study of Adaptive Neuro Fuzzy Inference System and Support Vector Regression for Forecasting Wind Power

Year 2019, Volume: 7 Issue: 1, 9 - 14, 31.03.2019

Hasan Huseyin Cevik , Mehmet Çunkaş

Abstract

The forecast of the power generated by a wind power plant is a process
that wind farm companies need to do every day. Electrical system manager uses
these forecasts to plan the next day’s electrical generation. Thus, while
generation-consumption balance in the grid is maintained, numbers of reserve
power plants are decreased. Wind power has uncertainty as it depends on nature.
Therefore, wind speed forecasts and wind direction forecasts of the power plant
area are generally used in wind power forecasts. In this study, hourly wind
power generation of next day is forecasted by using Adaptive Neuro Fuzzy
Inference System (ANFIS) and Support Vector Regression (SVR) methods. The hour
of day, wind speed forecast and wind direction forecast are the inputs of the forecast
system. One-year data are selected as training data, six-mount data are
forecasted. Five different models are formed by using the system inputs in
different configurations and final forecast are found by averaging the model
forecasts. The average normalized mean absolute error values are found 10.86%
and %10.8 with ANFIS and SVR, respectively. 

Keywords

Adaptive Neuro Fuzzy Inference System, ANFIS, Energy management, Support Vector Regression

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