Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82255
Title: Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system
Authors: Nabipour, N
Mosavi, A
Hajnal, E
Nadai, L
Shamshirband, S
Chau, K 
Issue Date: 2020
Source: Engineering applications of computational fluid mechanics, 1 Jan. 2020, v. 14, no. 1, p. 491-506
Abstract: Climate change impacts and adaptations are ongoing issues that are attracting the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken into consideration. An adaptive neuro-fuzzy inference system (ANFIS)-based post-processing technique was used to match the power outputs of the regional climate model (RCM) with those obtained from reference data. The near-surface wind data obtained from an RCM were used to investigate climate change impacts on the wind power resources in the Caspian Sea. After converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results were investigated to reveal mean annual power, seasonal and monthly variability for 20 year historical (1981-2000) and future (2081-2100) periods. The results revealed that climate change does not notably affect the wind climate over the study area. However, a small decrease was projected in the future simulation, revealing a slight decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution, with winter and summer having the highest values. The results indicate that the middle and northern parts of the Caspian Sea have the highest values of wind power. However, the results of the post-processing technique using the ANFIS model showed that the real potential of wind power in the area is lower than that projected in the RCM.
Keywords: Wind turbine
Adaptive neuro-fuzzy inference system (ANFIS)
Dynamical downscaling
Regional climate change model
Renewable energy
Machine learning
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2020.1722241
Rights: © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Narjes Nabipour, Amir Mosavi, Eva Hajnal, Laszlo Nadai, ShahaboddinShamshirband & Kwok-Wing Chau (2020) Modeling climate change impact on wind powerresources using adaptive neuro-fuzzy inference system, Engineering Applications of ComputationalFluid Mechanics, 14:1, 491-506 is available at https://dx.doi.org/10.1080/19942060.2020.1722241
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