MODELING AND FORECASTING OF WIND SPEED USING ARIMA AND SARIMA MODELS: AN EMPIRICAL STUDY OF CHENNAI CITY
DOI:
https://doi.org/10.65327/cse.v12i1.2478Keywords:
Wind power, NIWE, time series method, ARIMA, SARIMA, MSE, RMSE, wind speed forecasting, renewable energy, ChennaiAbstract
Wind energy is the strongest renewable energy source which ensures clean and safe production of energy. Wind speed prediction has an important place in wind energy systems and to drive turbines that are further helpful for generating electricity, but the issue with the system is that power generated from wind is uncertain. So, accurate wind speed forecasting is required to produce more electric power. This paper describes an empirical study of modeling and forecasting of wind speed of Chennai city using data provided by the National Institute of Wind Energy (NIWE) under The Ministry of New and Renewable Energy (MNRE). Two wind speed prediction models—Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA)—are built and evaluated using Mean Square Error (MSE) and Root Mean Square Error (RMSE) to identify the better forecasting model. The results conclusively demonstrate that the ARIMA(3, 0, 2) model outperforms the SARIMA(0, 1, 2)(0, 1, 2, 4) model in both test datasets, yielding significantly lower error values. The study employs three years of daily wind speed data (2015–2017) collected from NIWE's meteorological mast at Pallikaranai, Chennai.
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