Journal
JOURNAL OF CLEANER PRODUCTION
Volume 244, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.118573
Keywords
Wind turbine capacity; Energy economics; Grey system model; Particle swarm optimization
Categories
Funding
- National Natural Science Foundation of China [71901184, 71771033, 71571157, 11601357]
- Humanities and Social Science Project of Ministry of Education of China [19YJCZH119]
- V.C. AMP
- V.R. Key Lab of Sichuan Province [SCVCVR2018.10VS]
- National Statistical Scientific Research Project [2018LY42]
- State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation [PLN201710]
- Longshan academic talent research supporting program of SWUST [17LZXY20]
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Wind energy makes a significant contribution to global power generation. Predicting wind turbine capacity is becoming increasingly crucial for cleaner production. For this purpose, a new information priority accumulated grey model with time power is proposed to predict short-term wind turbine capacity. Firstly, the computational formulas for the time response sequence and the prediction values are deduced by grey modeling technique and the definite integral trapezoidal approximation formula. Secondly, an intelligent algorithm based on particle swarm optimization is applied to determine the optimal nonlinear parameters of the novel model. Thirdly, three real numerical examples are given to examine the accuracy of the new model by comparing with six existing prediction models. Finally, based on the wind turbine capacity from 2007 to 2017, the proposed model is established to predict the total wind turbine capacity in Europe, North America, Asia, and the world. The numerical results reveal that the novel model is superior to other forecasting models. It has a great advantage for small samples with new characteristic behaviors. Besides, reasonable suggestions are put forward from the standpoint of the practitioners and governments, which has high potential to advance the sustainable improvement of clean energy production in the future. (C) 2019 Elsevier Ltd. All rights reserved.
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