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Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements

Abstract:

We present a new Bayesian modeling approach for joint analysis of wind components and short-term wind prediction. This approach considers a truncated bivariate matrix Bayesian dynamic linear model (TMDLM) that jointly models the u (zonal) and v (meridional) wind components of observed hourly wind speed and direction data. The TMDLM takes into account calm wind observations and provides joint forecasts of hourly wind speed and direction at a given location. The proposed model is compared to alternative empirically-based time series approaches that are often used for short-term wind prediction, including the persistence method (naive predictor), as well as univariate and bivariate ARIMA models. Model performance is measured predictively in terms of mean squared errors associated to 1-h and 24-h ahead forecasts. We show that our approach generally leads to more accurate short term predictions than these alternative approaches in the context of analysis and forecasting of hourly wind measurements in 3 locations in Northern California for winter and summer months.