Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements
Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements
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DOI：
10.1016/j.renene.2020.05.182
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发表时间：
2020-12-01
期刊：
影响因子：
8.7
通讯作者：
Bravo, Lelys
中科院分区：
文献类型：
--
作者：
Garcia, Irene;Huo, Stella;Bravo, Lelys
我们提出了一种新的贝叶斯建模方法，用于联合分析风分量和短期风预测。这种方法认为，截断双变量矩阵贝叶斯动态线性模型（TMDLM），共同模拟的u（纬向）和v（纬向）风分量观测每小时的风速和风向数据。TMDLM考虑了静风观测，并提供给定位置每小时风速和风向的联合预报。所提出的模型相比，替代的基于时间序列的方法，经常用于短期风预测，包括持久性方法（天真的预测），以及单变量和双变量ARIMA模型。模型性能通过与1小时和24小时预测相关的均方误差进行预测性衡量。我们表明，我们的方法通常会导致更准确的短期预测比这些替代方法的分析和预测的背景下，在北方加州的冬季和夏季的3个地点的每小时风测量。(C)2020爱思唯尔有限公司保留所有权利。
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. (C) 2020 Elsevier Ltd. All rights reserved.