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Register a new userForecasting Monthly Wind Velocity in Tartous station using Box-Jenkins Methodology
التنبؤ بسرعة الرياح الشهرية في محطة طرطوس باستخدام منهجية بوكس - جنكنز
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آمال حيدر( باحث )
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تؤثر سرعة الرياح و اتجاهها بشكل كبير على الملاحة البحرية و حركة السفن التجارية في المرافئ، كما تؤثر على سرعة انتقال الملوثات في الهواء من المدن الصناعية إلى المناطق الزراعية و السكنية. تأتي أهمية البحث من إمكانية التنبؤ بسرعة الرياح الشهرية في محطة طرطوس، و لتحقيق هذا الهدف فقد استُخدمت بيانات السلسلة الزمنية لمعدل سرعة الرياح الشهرية في محطة طرطوس للفترة بين عامي1998-2003، و قد استُخدمت في الدراسة منهجية بوكس – جنكنز التي تعتمد على إيجاد التنبؤات المستقبلية لسلسلة البيانات الأصلية. كما تم استخدام البرامج Minitab و Excel للحصول على نتائج الدراسة. توصلت الدراسة إلى أن سرعة الرياح في محطة طرطوس متناقصة و قد بلغ هذا التناقص 0.002 كم/سا في الشهر خلال فترة الرصد، كما توصلت إلى بناء نموذج (SARIMA) المناسب للسلسلة بعد أن اجتاز مختلف الاختبارات الإحصائية المطلوبة، و كان النموذج SARIMA(1,0,0)(1,1,1)12 هو النموذج المناسب لتمثيل البيانات و النموذج SARIMA(1,0,1)(1,1,0)12 هو النموذج المناسب للتنبؤ بسرعة الرياح الشهرية المستقبلية.
The velocity and direction of the wind greatly affect marine navigation and the movement of merchant ships in harbors, It also affects the rapid movement of pollutants into the air from industrial cities to agricultural and residential areas. The importance of the research comes from forecasting monthly wind velocity in the Tartous station and to achieve this goal the data of time series for the monthly wind velocity at Tartous station in Tartous governorate The methodology of "Box – Jenkins" been used in the study, this methodology relies on finding future forecasts from original data series. Also, the applications “MINITAB, EXCEL” have been used to obtain the results of the study. As a result, the study found that wind velocity value in the ' Tartous station' decreasing, this decline amounted to 0.002 km/h per month during the monitoring period. Also, the appropriate (SARIMA) model for the series was build after it passed the various statistical tests are required, and founded that SARIMA(1,0,0)(1,1,1)12 model is a good representation of the data and the SARIMA(1,0,1)(1,1,0)12 model is the right model to forecast future monthly wind.
References used
KAVASSERI، R. G.;SEETHARAMAN، K. Day-ahead Wind Speed Forecasting Using F-ARIMA Models. North Dakota State University ، 2009
WANG، H.; YAN، J.; LIU، Y.; HAN، SH.; ZHAO، J. Multi-Step-Ahead Method For Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data. 2017
FALK، M.; MAROHN، F.; MICHEL، R.; HOFMANN، D.; MACKE، M. A First Course on Time Series Analysis. Chair of Statistics، University of Wurzburg، 2006. 58-76
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