Climatic Long Term Trend and Prediction of the Wind Energy Resource in the Gwadar Port

ZHENG Chongwei1,2,3,†, GAO Yue4, CHEN Xuan1

ACTA Scientiarum Naturalium Universitatis Pekinensis - - Contents - ZHENG Chongwei, GAO Yue, CHEN Xuan

1. College of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing 211101; 2. Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640; 3. Navigation Department, Dalian Naval Academy, Dalian 116018; 4. China Ocean News, Beijing 100860; † E-mail:

Abstract Based on the 36-year (19792014) Era-interim 10 m sea surface wind data from European Centre for Medium-range Weather Forecasts (ECMWF), the climatic long term trend of the wind energy resource of the Gwadar Port of Pakistan was analyzed. Using two linear regression and artificial neural network (ANN) techniques, the wind energy resource in the long term was predicted. The results show that wind energy in summer is richer than that in winter. The stability in summer is better than that in winter. For the past 36 years, the wind power density, occurrence of effective wind speed and energy level occurrence have significant annual decreasing trends, of −0.78 W/(m2 · a), −0.21%/a, −0.22%/a separately. These trends mainly exhibit in summer, while no significant variation in winter. The stability (coefficient of variation, monthly variability index and seasonal variability index) does not have a significant long term trend for the past 36 years. From the prediction value, the wind energy resource in 2015 is similar to the multi-year average value, while the wind energy resource in 2016 is richer than the multi-year average value. For the year 20152016, the prediction wind energy will be more unstable than the multi-year average status. The results can provide scientific reference for the 21st Maritime Silk Road construction, development of remote islands and ports in the China seas. Key words 21st Maritime Silk Road; Gwadar Port; wind energy resource; climatic long term trend; prediction

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