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WindDragon: Enhancing wind power forecasting with Automated Deep Learning

Published 22 Feb 2024 in cs.LG, physics.ao-ph, and stat.ML | (2402.14385v1)

Abstract: Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) windpower forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.

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Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl. CoRR, abs/2006.13799, 2020. URL https://arxiv.org/abs/2006.13799. Y. LeCun, Y. Bengio, et al. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995, 1995. Liu et al. [2018] H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu. Hierarchical representations for efficient architecture search, 2018. Liu et al. [2019] H. Liu, K. Simonyan, and Y. Yang. Darts: Differentiable architecture search, 2019. Piotrowski et al. [2022] P. Piotrowski, D. Baczyński, M. Kopyt, and T. Gulczyński. Advanced ensemble methods using machine learning and deep learning for one-day-ahead forecasts of electric energy production in wind farms. Energies, 15(4):1252, 2022. Riahy and Abedi [2008] G. Riahy and M. Abedi. Short term wind speed forecasting for wind turbine applications using linear prediction method. Renewable energy, 33(1):35–41, 2008. Shi et al. [2012] J. Shi, J. Guo, and S. Zheng. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5):3471–3480, 2012. Statista Research Department [2022] Statista Research Department. Europe: Electricity demand per capita 2022. https://www.statista.com/statistics/1262471/per-capita-electricity-consumption-europe/, 2022. Tawn and Browell [2022] R. Tawn and J. Browell. A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153:111758, 2022. ISSN 1364-0321. doi: https://doi.org/10.1016/j.rser.2021.111758. URL https://www.sciencedirect.com/science/article/pii/S1364032121010285. United Nations Convention on Climate Change [2015] United Nations Convention on Climate Change. Paris Agreement. Climate Change Conference (COP21), Dec. 2015. URL https://unfccc.int/sites/default/files/english_paris_agreement.pdf. Vaswani et al. 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Hierarchical representations for efficient architecture search, 2018. Liu et al. [2019] H. Liu, K. Simonyan, and Y. Yang. Darts: Differentiable architecture search, 2019. Piotrowski et al. [2022] P. Piotrowski, D. Baczyński, M. Kopyt, and T. Gulczyński. Advanced ensemble methods using machine learning and deep learning for one-day-ahead forecasts of electric energy production in wind farms. Energies, 15(4):1252, 2022. Riahy and Abedi [2008] G. Riahy and M. Abedi. Short term wind speed forecasting for wind turbine applications using linear prediction method. Renewable energy, 33(1):35–41, 2008. Shi et al. [2012] J. Shi, J. Guo, and S. Zheng. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5):3471–3480, 2012. Statista Research Department [2022] Statista Research Department. Europe: Electricity demand per capita 2022. https://www.statista.com/statistics/1262471/per-capita-electricity-consumption-europe/, 2022. Tawn and Browell [2022] R. Tawn and J. Browell. A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153:111758, 2022. ISSN 1364-0321. doi: https://doi.org/10.1016/j.rser.2021.111758. URL https://www.sciencedirect.com/science/article/pii/S1364032121010285. United Nations Convention on Climate Change [2015] United Nations Convention on Climate Change. Paris Agreement. Climate Change Conference (COP21), Dec. 2015. URL https://unfccc.int/sites/default/files/english_paris_agreement.pdf. Vaswani et al. [2017] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Wang et al. [2021] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu. A review of wind speed and wind power forecasting with deep neural networks. Applied Energy, 304:117766, 2021. ISSN 0306-2619. doi: https://doi.org/10.1016/j.apenergy.2021.117766. URL https://www.sciencedirect.com/science/article/pii/S0306261921011053. Zhang [2019] R. Zhang. Making convolutional networks shift-invariant again. In International conference on machine learning, pages 7324–7334. PMLR, 2019. Zimmer et al. [2020] L. Zimmer, M. Lindauer, and F. Hutter. Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl. CoRR, abs/2006.13799, 2020. URL https://arxiv.org/abs/2006.13799. H. Liu, K. Simonyan, and Y. Yang. Darts: Differentiable architecture search, 2019. Piotrowski et al. [2022] P. Piotrowski, D. Baczyński, M. Kopyt, and T. Gulczyński. Advanced ensemble methods using machine learning and deep learning for one-day-ahead forecasts of electric energy production in wind farms. Energies, 15(4):1252, 2022. Riahy and Abedi [2008] G. Riahy and M. Abedi. Short term wind speed forecasting for wind turbine applications using linear prediction method. Renewable energy, 33(1):35–41, 2008. Shi et al. [2012] J. Shi, J. Guo, and S. Zheng. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5):3471–3480, 2012. Statista Research Department [2022] Statista Research Department. Europe: Electricity demand per capita 2022. https://www.statista.com/statistics/1262471/per-capita-electricity-consumption-europe/, 2022. Tawn and Browell [2022] R. Tawn and J. Browell. A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153:111758, 2022. ISSN 1364-0321. doi: https://doi.org/10.1016/j.rser.2021.111758. URL https://www.sciencedirect.com/science/article/pii/S1364032121010285. United Nations Convention on Climate Change [2015] United Nations Convention on Climate Change. Paris Agreement. Climate Change Conference (COP21), Dec. 2015. URL https://unfccc.int/sites/default/files/english_paris_agreement.pdf. Vaswani et al. [2017] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Wang et al. [2021] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu. A review of wind speed and wind power forecasting with deep neural networks. Applied Energy, 304:117766, 2021. ISSN 0306-2619. doi: https://doi.org/10.1016/j.apenergy.2021.117766. URL https://www.sciencedirect.com/science/article/pii/S0306261921011053. Zhang [2019] R. Zhang. Making convolutional networks shift-invariant again. In International conference on machine learning, pages 7324–7334. PMLR, 2019. Zimmer et al. [2020] L. Zimmer, M. Lindauer, and F. Hutter. Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl. CoRR, abs/2006.13799, 2020. URL https://arxiv.org/abs/2006.13799. P. Piotrowski, D. Baczyński, M. Kopyt, and T. Gulczyński. Advanced ensemble methods using machine learning and deep learning for one-day-ahead forecasts of electric energy production in wind farms. Energies, 15(4):1252, 2022. Riahy and Abedi [2008] G. Riahy and M. Abedi. Short term wind speed forecasting for wind turbine applications using linear prediction method. Renewable energy, 33(1):35–41, 2008. Shi et al. [2012] J. Shi, J. Guo, and S. Zheng. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5):3471–3480, 2012. Statista Research Department [2022] Statista Research Department. Europe: Electricity demand per capita 2022. https://www.statista.com/statistics/1262471/per-capita-electricity-consumption-europe/, 2022. Tawn and Browell [2022] R. Tawn and J. Browell. A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153:111758, 2022. ISSN 1364-0321. doi: https://doi.org/10.1016/j.rser.2021.111758. URL https://www.sciencedirect.com/science/article/pii/S1364032121010285. United Nations Convention on Climate Change [2015] United Nations Convention on Climate Change. Paris Agreement. Climate Change Conference (COP21), Dec. 2015. URL https://unfccc.int/sites/default/files/english_paris_agreement.pdf. Vaswani et al. [2017] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Wang et al. [2021] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu. A review of wind speed and wind power forecasting with deep neural networks. Applied Energy, 304:117766, 2021. ISSN 0306-2619. doi: https://doi.org/10.1016/j.apenergy.2021.117766. URL https://www.sciencedirect.com/science/article/pii/S0306261921011053. Zhang [2019] R. Zhang. Making convolutional networks shift-invariant again. In International conference on machine learning, pages 7324–7334. PMLR, 2019. Zimmer et al. [2020] L. Zimmer, M. Lindauer, and F. Hutter. Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl. CoRR, abs/2006.13799, 2020. URL https://arxiv.org/abs/2006.13799. G. Riahy and M. Abedi. Short term wind speed forecasting for wind turbine applications using linear prediction method. Renewable energy, 33(1):35–41, 2008. Shi et al. [2012] J. Shi, J. Guo, and S. Zheng. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5):3471–3480, 2012. Statista Research Department [2022] Statista Research Department. Europe: Electricity demand per capita 2022. https://www.statista.com/statistics/1262471/per-capita-electricity-consumption-europe/, 2022. Tawn and Browell [2022] R. Tawn and J. Browell. A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153:111758, 2022. ISSN 1364-0321. doi: https://doi.org/10.1016/j.rser.2021.111758. URL https://www.sciencedirect.com/science/article/pii/S1364032121010285. United Nations Convention on Climate Change [2015] United Nations Convention on Climate Change. Paris Agreement. Climate Change Conference (COP21), Dec. 2015. URL https://unfccc.int/sites/default/files/english_paris_agreement.pdf. Vaswani et al. [2017] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Wang et al. [2021] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu. A review of wind speed and wind power forecasting with deep neural networks. Applied Energy, 304:117766, 2021. ISSN 0306-2619. doi: https://doi.org/10.1016/j.apenergy.2021.117766. URL https://www.sciencedirect.com/science/article/pii/S0306261921011053. Zhang [2019] R. Zhang. Making convolutional networks shift-invariant again. In International conference on machine learning, pages 7324–7334. PMLR, 2019. Zimmer et al. [2020] L. Zimmer, M. Lindauer, and F. Hutter. Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl. CoRR, abs/2006.13799, 2020. URL https://arxiv.org/abs/2006.13799. J. Shi, J. Guo, and S. Zheng. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5):3471–3480, 2012. Statista Research Department [2022] Statista Research Department. Europe: Electricity demand per capita 2022. https://www.statista.com/statistics/1262471/per-capita-electricity-consumption-europe/, 2022. Tawn and Browell [2022] R. Tawn and J. Browell. A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153:111758, 2022. 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