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Proceedings Papers

Wind Farm Clustering Methods for Power Forecasting

Navid Goudarzi, Dorsa Ziaei
Proc. ASME. POWER2022, ASME 2022 Power Conference, V001T07A015, July 18–19, 2022
Paper No: POWER2022-86666
https://doi.org/10.1115/POWER2022-86666
View Paper titled, Wind Farm Clustering Methods for Power Forecasting
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Topics: Spatiotemporal phenomena, Wind farms
Proceedings Papers

Short-Term Wind Characteristics Forecasting Using Stacked LSTM Networks

Dorsa Ziaei, Navid Goudarzi
Proc. ASME. POWER2021, ASME 2021 Power Conference, V001T09A013, July 20–22, 2021
Paper No: POWER2021-65866
https://doi.org/10.1115/POWER2021-65866
View Paper titled, Short-Term Wind Characteristics Forecasting Using Stacked LSTM Networks
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Topics: Renewable energy, Wind, Databases, Errors, Modeling, Ocean engineering, Offshore wind turbines, Physics, Pipelines, Time series
Proceedings Papers

A Take on Wake Modeling of Turbines Based on Deep Learning

Dorsa Ziaei, Navid Goudarzi
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T10A017, August 4–5, 2020
Paper No: POWER2020-16950
https://doi.org/10.1115/POWER2020-16950
View Paper titled, A Take on Wake Modeling of Turbines Based on Deep Learning
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Topics: Modeling, Turbines, Turbulence, Wakes
Proceedings Papers

Artificial Intelligence for Optimal Sitting of Individual and Networks of Wind Farms

Seyyed Pooya Hekmati Athar, Dorsa Ziaei, Navid Goudarzi
Proc. ASME. POWER2019, ASME 2019 Power Conference, V001T06A013, July 15–18, 2019
Paper No: POWER2019-1948
https://doi.org/10.1115/POWER2019-1948
View Paper titled, Artificial Intelligence for Optimal Sitting of Individual and Networks of Wind Farms
Open the PDF for Artificial Intelligence for Optimal Sitting of Individual and Networks of Wind Farms in another window
Topics: Algorithms, Artificial intelligence, Bone, Fireworks, Optimization, Renewable energy, Wind farms
Proceedings Papers

Assessment of a CFD-Based Machine Learning Approach on Turbulent Flow Approximation

Dorsa Ziaei, Seyyed Pooya Hekmati Athar, Navid Goudarzi
Proc. ASME. ES2019, ASME 2019 13th International Conference on Energy Sustainability, V001T14A003, July 14–17, 2019
Paper No: ES2019-3925
https://doi.org/10.1115/ES2019-3925
View Paper titled, Assessment of a CFD-Based Machine Learning Approach on Turbulent Flow Approximation
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Topics: Approximation, Artificial neural networks, Computational fluid dynamics, Machine learning, Turbulence