COMPARING TIME SERIES FORECASTING METHODS TO ESTIMATE WIND SPEED IN KIRIKKALE REGION

Due to the non-storable nature of electric energy, short-term and long-term electricity generation and consumption forecast are critical to keeping electricity market in balance. In addition, the production estimate of wind energy is parallel to the estimate of wind speed. Since wind speed forecasts includes seasonal and time-dependent trends, time series forecasting methods produce successful results in wind energy forecasting. However, choosing the most appropriate time series forecasting method for short-term and long-term production forecasts is of special importance. In this study, short-term and long-term wind speed estimations were made for the wind turbine at Kirikkale University by using Exponential Smoothing (ES) and ARMA (Auto Regressive Moving Average) methods. The most suitable methods for forecasting short-term and long-term wind speed have been determined with the obtained results.