Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference System
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ID: 281660
2020
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Abstract
Climate change impacts and adaptations are the subjects 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 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.
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| Authors | Narjes Nabipour; Amir Mosavi; Eva Hajnal; Laszlo Nadai; Shahab Shamshirband; Kwok-Wing Chau |
| Journal | arXiv |
| Year | 2020 |
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