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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 pbkp_rediction. 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 pbkp_rediction, including the persistence method (naive pbkp_redictor), as well as univariate and bivariate ARIMA models. Model performance is measured pbkp_redictively 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 pbkp_redictions 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.