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基于连续 NWP 预测的面向交通管制决策支持系统的短期风速集中订正算法。

FOCUSED-Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems.

机构信息

Faculty of Information Studies in Novo Mesto, 8000 Novo Mesto, Slovenia.

Department for Information Systems and Business Analytics, Algebra University College, 10000 Zagreb, Croatia.

出版信息

Sensors (Basel). 2021 May 13;21(10):3405. doi: 10.3390/s21103405.

Abstract

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm's performance on a data set comprising five years of real data measurements at the Croatian bridge "Krk" and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model.

摘要

在本文中,我们提出了一种新的算法,称为 FOCUSED(使用连续预测进行预测校正),用于短期风速预测的校正。我们开发 FOCUSED 的目的是改善暴洪阵风的预测,暴洪阵风经常导致对交通危险的高速风情况。这一动机源于目前部署的决策支持系统偶尔出现的不明确结果,该系统在克罗地亚沿海强风和阵风条件下协助交通管理。所提出的校正算法使用数值天气预报模型的特征多次对同一未来窗口的风速进行迭代预测。我们将这些迭代预测用作 FOCUSED 算法的输入特征,并将校正后的预测作为输出。我们将提出的算法与人工神经网络、随机森林、支持向量机和线性回归进行了比较,以证明该算法在包含五年克罗地亚桥梁“Krk”实际数据测量和由 ALADIN(Aire Limitée Adaptation dynamique Développement InterNational)数值天气预报模型进行的补充历史预测的数据集上的性能优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ea8/8153314/e7e2d64208f1/sensors-21-03405-g001.jpg

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