Preprint Article Version 1 This version is not peer-reviewed

Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference System

Version 1 : Received: 9 January 2020 / Approved: 11 January 2020 / Online: 11 January 2020 (10:15:40 CET)

How to cite: Nabipour, N.; Mosavi, A.; Hajnal, E.; Nadai, L.; Shamshirband, S.; Chau, K. Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference System. Preprints 2020, 2020010100 (doi: 10.20944/preprints202001.0100.v1). Nabipour, N.; Mosavi, A.; Hajnal, E.; Nadai, L.; Shamshirband, S.; Chau, K. Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference System. Preprints 2020, 2020010100 (doi: 10.20944/preprints202001.0100.v1).

Abstract

Climate change impacts and adaptations is subject to ongoing issues that attract 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 under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly 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 when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

Subject Areas

wind turbine; adaptive neuro-fuzzy inference system (ANFIS); dynamical downscaling; regional climate change model; renewable energy; machine learning

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