Autoregressive Models for Forecasting Wind Speed in the Rural Area of Socabaya, Peru, 2022

Abstract

The rural area of Peru presents a great variability of winds, its ignorance due to the absence of prediction models has an unfavorable effect on agriculture, infrastructure, security, transportation and optimization of wind energy. This work obtains an autoregressive integrated moving average (ARIMA) model for the prediction of wind speed in the R programming language for the rural area of Peru, Socabaya district of Arequipa. The methodology consists of the quantitative method and documentary technique, with a sample of 334 wind data from the year 2022 from the National Aeronautics and Space Administration (NASA) meteorological station (POWER, 2023) for the Socabaya district. Using R, the parametric methods Dickey-Fuller, Levene, D'Agostino, Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and differencing (d=1) were applied to achieve normality and stationarity of the data. The simple autocorrelation function (ACF) and partial autocorrelation function (Partial ACF) are analyzed by means of a recursive adjustment process, Akaike's Information Criterion (AIC) to choose the best ARIMA prediction model. The result obtained is the ARIMA (1, 1, 2) wind prediction model, with a mean absolute scale error (MASE) precision of 0.849. It is concluded that the ARIMA model obtained can be used to predict the wind speed in Socabaya in the short term, from November 29 to December 8, 2022, and its randomness would be influenced by climate variability and amount of data from the year 2022.