Research on Short-Time Wind Speed Prediction in Mountainous Areas Based on Improved ARIMA Model

Zelin Zhou, Yiyan Dai (), Jun Xiao, Maoyi Liu, Jinxiang Zhang and Mingjin Zhang
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Zelin Zhou: China 19th Metallurgical Corporation, Chengdu 610031, China
Yiyan Dai: Department of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, China
Jun Xiao: CCCC Second Highway Engineering Co., Ltd., Xi’an 710199, China
Maoyi Liu: Chongqing Construction Investment (Group) Co., Ltd., Chongqing 400010, China
Jinxiang Zhang: Department of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, China
Mingjin Zhang: Department of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, China

Sustainability, 2022, vol. 14, issue 22, 1-12

Abstract: In rugged mountain areas, the lateral aerodynamic force and aerodynamic lift caused by strong winds are the main reasons for the lateral overturning of trains and the destruction of buildings and structures along the railroad line. Therefore, it is important to build a strong wind alarm system along the railroad line, and a reasonable and accurate short-time forecast of a strong wind is the basis of it. In this research, two methods of constructive function and time-series decomposition are proposed to pre-process the input wind speed for periodic strong winds in mountainous areas. Then, the improved Auto-Regressive Integrated Moving Average model time-series model was established through the steps of a white noise test, data stationarity test, model recognition, and order determination. Finally, the effectiveness of the improved wind speed prediction was examined. The results of the research showed that rational choice of processing functions has a large impact on wind speed prediction results. The prediction accuracy of the improved ARIMA model proposed in this paper is better than the results of the traditional Seasonal Auto-Regressive Integrated Moving Average model, and it can quickly and accurately realize the short-time wind speed prediction along the railroad line in rugged mountains. In addition, the improved ARIMA model has verified its universality in different mountainous places.

Keywords: wind speed forecast; data pre-processing; auto-regressive integrated moving average; seasonal auto-regressive integrated moving average; rugged mountain areas (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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