Most influential parametrical and data needs for realistic wind speed prediction |
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| Affiliation: | 1. Centre for Energy Sciences, Department of Mechanical Engineering, Faculty of Engineering, 50603 Kuala Lumpur University of Malaya, Malaysia;2. Mechanical Engineering Department, Collage of Engineering, King Saud University, 11421 Riyadh, Saudi Arabia;3. Dept. of Mechanical Engineering, Dhaka University of Engineering and Technology, Gazipur, 1700, Bangladesh;1. South China University of Technology, Wushan Road 381#, Tianhe District, Guangzhou 510640, China;2. Jiangsu University, Jiangsu, Xuefu Road 301#, Jingkou District, Zhenjiang 212013, China;1. Ocean Engineering and Technology Research Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran;2. Griffith School of Engineering, Gold Coast Campus, Griffith University, QLD, 4222, Australia;1. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore;2. Water Desalination & Reuse (WDR) Center King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;1. Centro Nacional de Investigación y Desarrollo Tecnológico, CENIDET-TecNM-SEP, Prol. Av. Palmira S/N, Col. Palmira, Cuernavaca, Morelos CP 62490, Mexico;2. Universidad de Sonora, UNISON, Blvd. Rosales y Luis Encinas, Hermosillo, Sonora CP 83000, Mexico;3. Instituto Tecnológico de Zacatepec, ITZ-TecNM-SEP Calzada Tecnológico No. 27, Zacatepec de Hidalgo, Morelos, CP 62780, Mexico;1. National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Locked Bag 1395, Launceston, Tasmania, 7250, Australia;2. National Centre for Ports and Shipping, Australian Maritime College, University of Tasmania, Locked Bag 1395, Launceston, Tasmania, 7250, Australia;3. Dept. of Mechanical Engineering, University College London, Torrington Place, WC1E 7JE, London, United Kingdom |
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| Abstract: | Depleting fossil fuel reserves and increasing global weather concerns has diverted mankind to look out for clean and green reserves of energy ever since the beginning of last decade. Wind holds a major role in satisfying our energy needs, however, its use as an alternate power source accounts for various issues such as deregulation of supply, frequency instability, etc. In order to nullify such effects, power engineers need to have an idea of futuristic weather conditions, especially the wind speed trend. Numerical Weather Prediction (NWP) tools such as Yearly Auto-Regressive (YAR) models when deployed for medium-term wind speed forecasting have proved their effectiveness. In this paper Artificial Neural Network based Yearly Auto-Regressive (ANNYAR) model have been used to figure out the most influential parameter's affecting wind prediction and corresponding range of yearly data set required for Time Horizon (TH) extending from 6 to 96 h. Data from area in and around ‘VABB airfield Mumbai’ has been incorporated for modelling and analysis purpose. |
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| Keywords: | Artificial neural network Auto regressive Multi-layer perceptron neural network Numerical weather prediction Parametrical combination Time horizon |
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