Machine Learning-Based Forecasting of Renewable Power Generation Using Meteorological Data
Research Article
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Machine Learning-Based Forecasting of Renewable Power Generation Using Meteorological Data

Han Lyu 1*
1 Department of Statistics, University of Illinois Urbana-Champaign, Illinois, America
*Corresponding author: hanlyu2@illinois.edu
Published on 22 October 2025
Journal Cover
ACE Vol.196
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-451-9
ISBN (Online): 978-1-80590-452-6
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Abstract

This study tackles short-term wind power forecasting using hourly data (2017–2021) from four utility-scale sites. An end-to-end machine learning pipeline is constructed with strict quality control and physics-informed features, including u/v wind vector decomposition, nonlinear wind-speed terms, and compact calendar encodings (hour and month). Model evaluation combines rolling-origin time splits with leave-one-site-out (LOSO) cross-site testing. Stable feature importance analysis yields a Top-27 feature set, ensuring comparability across sites and deployment readiness. In pooled training, LightGBM performs best (RMSE ≈ 0.161, R² ≈ 0.45), while results reveal strong site heterogeneity. LOSO testing improves generalization at lower-skill sites, and adding time encodings further tightens per-site fits (R² ≈ 0.98). The contribution is a reproducible forecasting workflow that balances physical interpretability with predictive accuracy, a lean, transferable feature set, and a rigorous evaluation protocol that separates temporal from spatial generalization. Findings inform operational forecasting for wind assets and offer a practical blueprint for scaling predictive maintenance and dispatch decisions across diverse wind regimes.

Keywords:

Renewable energy forecasting, Wind power prediction, Machine learning models, Cross-site generalization.

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Lyu,H. (2025). Machine Learning-Based Forecasting of Renewable Power Generation Using Meteorological Data. Applied and Computational Engineering,196,47-60.

References

[1]. Olson, J. B., Kenyon, J. S., Angevine, W. M., Brown, J. M., Pagowski, M., & Suselj, K. (2019). Improving wind energy forecasting through numerical weather prediction model development. Bulletin of the American Meteorological Society, 100(11), 2201–2220. https: //doi.org/10.1175/BAMS-D-18-0040.1

[2]. Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Manikin, G. S., Sponseller, D. W., Smith, T. L., Moninger, W. R., Kenyon, J., & Jamison, B. D. (2016). A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Monthly Weather Review, 144(4), 1669–1693. https: //doi.org/10.1175/MWR-D-15-0242.1

[3]. Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., & Huang, X.-Y. (2019). A description of the Advanced Research WRF model version 4 (NCAR Technical Note NCAR/TN-556+STR). National Center for Atmospheric Research. https: //opensky.ucar.edu/islandora/object/technotes%3A500

[4]. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https: //doi.org/10.1002/qj.3803

[5]. Draxl, C., Hodge, B.-M., Clifton, A., & McCaa, J. (2015). Overview and meteorological validation of the Wind Integration National Dataset (WIND) Toolkit (NREL/TP-5000-61740). National Renewable Energy Laboratory. https: //www.nrel.gov/docs/fy15osti/61740.pdf

[6]. Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2012). Current methods and advances in forecasting of wind power generation. Renewable Energy, 37(1), 1–8. https: //doi.org/10.1016/j.renene.2011.05.033

[7]. Meng, W., Li, C., Wang, J., Zhang, Y., & Zhao, C. (2022). Short-term wind power forecasting based on mixed data sampling and feature engineering. Energy, 244, 123195. https: //doi.org/10.1016/j.energy.2021.123195

[8]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https: //doi.org/10.1145/2939672.2939785

[9]. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017) (pp. 3146–3154). http: //papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

[10]. Perr-Sauer, J., Optis, M., Fields, M. J., Philips, C., Debnath, U., Martin, W., Letchford, J., & King, R. (2021). OpenOA: An open-source codebase for operational analysis of wind farms. Journal of Open Source Software, 6(58), 2171. https: //doi.org/10.21105/joss.02171

[11]. Morrison, R., Polikarpova, I., Infield, D., & Carroll, J. (2022). Anomaly detection in wind turbine SCADA data for power curve cleaning. Renewable Energy, 191, 701–717. https: //doi.org/10.1016/j.renene.2022.04.114

[12]. Yao, Q., Hu, Y., Wang, J., Yang, C., & Li, Y. (2023). A composed method of cleaning anomaly data for wind turbine SCADA. Renewable Energy, 211, 111–123. https: //doi.org/10.1016/j.renene.2023.04.044

[13]. Mardia, K. V., & Jupp, P. E. (2000). Directional statistics. John Wiley & Sons. https: //onlinelibrary.wiley.com/doi/book/10.1002/9780470316979

[14]. Wang, Z., Wang, Y., Ren, H., Zhang, L., Zhang, H., & Liang, J. (2024). Spatiotemporal wind speed forecasting using u-v components and graph attention networks. Scientific Reports, 14, 39519. https: //www.nature.com/articles/s41598-024-39519-9

[15]. Picard, A., Davis, R. S., Gläser, M., & Fujii, K. (2008). Revised formula for the density of moist air (CIPM-2007). Metrologia, 45(2), 149–155. https: //doi.org/10.1088/0026-1394/45/2/004

[16]. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https: //otexts.com/fpp3/

[17]. Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting, 16(4), 437–450. https: //doi.org/10.1016/S0169-2070(00)00065-0

[18]. Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929. https: //doi.org/10.1111/ecog.02881

[19]. Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016). Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. International Journal of Forecasting, 32(3), 896–913. https: //doi.org/10.1016/j.ijforecast.2016.02.001

[20]. Yang, X., Vano, J. A., Feldstein, S. B., Wu, Y., & Tippett, M. K. (2024). Skillful seasonal prediction of wind energy resources in the U.S. Great Plains. Communications Earth & Environment, 5, 263. https: //doi.org/10.1038/s43247-024-01457-w

[21]. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017) (pp. 4765–4774). https: //papers.nips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

References

[1]. Olson, J. B., Kenyon, J. S., Angevine, W. M., Brown, J. M., Pagowski, M., & Suselj, K. (2019). Improving wind energy forecasting through numerical weather prediction model development. Bulletin of the American Meteorological Society, 100(11), 2201–2220. https: //doi.org/10.1175/BAMS-D-18-0040.1

[2]. Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Manikin, G. S., Sponseller, D. W., Smith, T. L., Moninger, W. R., Kenyon, J., & Jamison, B. D. (2016). A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Monthly Weather Review, 144(4), 1669–1693. https: //doi.org/10.1175/MWR-D-15-0242.1

[3]. Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., & Huang, X.-Y. (2019). A description of the Advanced Research WRF model version 4 (NCAR Technical Note NCAR/TN-556+STR). National Center for Atmospheric Research. https: //opensky.ucar.edu/islandora/object/technotes%3A500

[4]. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https: //doi.org/10.1002/qj.3803

[5]. Draxl, C., Hodge, B.-M., Clifton, A., & McCaa, J. (2015). Overview and meteorological validation of the Wind Integration National Dataset (WIND) Toolkit (NREL/TP-5000-61740). National Renewable Energy Laboratory. https: //www.nrel.gov/docs/fy15osti/61740.pdf

[6]. Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2012). Current methods and advances in forecasting of wind power generation. Renewable Energy, 37(1), 1–8. https: //doi.org/10.1016/j.renene.2011.05.033

[7]. Meng, W., Li, C., Wang, J., Zhang, Y., & Zhao, C. (2022). Short-term wind power forecasting based on mixed data sampling and feature engineering. Energy, 244, 123195. https: //doi.org/10.1016/j.energy.2021.123195

[8]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https: //doi.org/10.1145/2939672.2939785

[9]. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017) (pp. 3146–3154). http: //papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

[10]. Perr-Sauer, J., Optis, M., Fields, M. J., Philips, C., Debnath, U., Martin, W., Letchford, J., & King, R. (2021). OpenOA: An open-source codebase for operational analysis of wind farms. Journal of Open Source Software, 6(58), 2171. https: //doi.org/10.21105/joss.02171

[11]. Morrison, R., Polikarpova, I., Infield, D., & Carroll, J. (2022). Anomaly detection in wind turbine SCADA data for power curve cleaning. Renewable Energy, 191, 701–717. https: //doi.org/10.1016/j.renene.2022.04.114

[12]. Yao, Q., Hu, Y., Wang, J., Yang, C., & Li, Y. (2023). A composed method of cleaning anomaly data for wind turbine SCADA. Renewable Energy, 211, 111–123. https: //doi.org/10.1016/j.renene.2023.04.044

[13]. Mardia, K. V., & Jupp, P. E. (2000). Directional statistics. John Wiley & Sons. https: //onlinelibrary.wiley.com/doi/book/10.1002/9780470316979

[14]. Wang, Z., Wang, Y., Ren, H., Zhang, L., Zhang, H., & Liang, J. (2024). Spatiotemporal wind speed forecasting using u-v components and graph attention networks. Scientific Reports, 14, 39519. https: //www.nature.com/articles/s41598-024-39519-9

[15]. Picard, A., Davis, R. S., Gläser, M., & Fujii, K. (2008). Revised formula for the density of moist air (CIPM-2007). Metrologia, 45(2), 149–155. https: //doi.org/10.1088/0026-1394/45/2/004

[16]. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https: //otexts.com/fpp3/

[17]. Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting, 16(4), 437–450. https: //doi.org/10.1016/S0169-2070(00)00065-0

[18]. Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929. https: //doi.org/10.1111/ecog.02881

[19]. Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016). Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. International Journal of Forecasting, 32(3), 896–913. https: //doi.org/10.1016/j.ijforecast.2016.02.001

[20]. Yang, X., Vano, J. A., Feldstein, S. B., Wu, Y., & Tippett, M. K. (2024). Skillful seasonal prediction of wind energy resources in the U.S. Great Plains. Communications Earth & Environment, 5, 263. https: //doi.org/10.1038/s43247-024-01457-w

[21]. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017) (pp. 4765–4774). https: //papers.nips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

Cite this article

Lyu,H. (2025). Machine Learning-Based Forecasting of Renewable Power Generation Using Meteorological Data. Applied and Computational Engineering,196,47-60.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-451-9(Print) / 978-1-80590-452-6(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.196
ISSN: 2755-2721(Print) / 2755-273X(Online)

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