Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

TLDR

Wind speed forecasting methods such as ARIMA have been extensively studied across many prediction problems. The study aimed to compare how meteorological variables influence a multivariate NARX wind‑speed model relative to a high‑performance univariate ARIMA model. Two models were developed: a univariate ARIMA and a multivariate NARX neural network that used barometric pressure, air temperature, wind direction, solar radiation or relative humidity, and lagged wind speed, trained on hourly data from La Mata and ten‑minute data from Metepec. The NARX model outperformed the ARIMA model, improving mean absolute error by 5.5–10 % for hourly data and 2.3–12.8 % for ten‑minute data, and reducing mean squared error by 6 % for hourly data and 2.3–12.8 % for ten‑minute data.

Abstract

Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.