A Machine Learning Approach for Identifying Favorable Sites for Renewable Energy Installations
DOI:
https://doi.org/10.58445/rars.682Keywords:
Machine Learning, Semi-supervised Learning, Renewable Energy, Wind Energy, Solar EnergyAbstract
This paper demonstrates the application of machine learning in determining suitability for utility scale sites for renewable energy production. Supervised algorithms such as Random Forest Classifier are employed in a Semi-Supervised learning process that allows underlying trends present in suitable and non suitable sites to be extrapolated to a mostly unlabeled dataset. The model iteratively trains from the pseudo labels it creates throughout this process, until all data points in the data set are labeled. This allows the small percentage of hand labeled data to be leveraged for use in the larger dataset. This open source tool can be used by anyone for the quick and precise determination of suitable locations for utility scale and personal installations based on the available renewable resources. It can also be used to influence future policy decisions around renewable energy.
References
Petros’yants, A. M. A pioneer of nuclear power, 1989.
Nuvolari, A.; Verspagen, B.; von Tunzelmann, N. The Early Diffusion of the Steam Engine in Britain, 1700–1800: A Reappraisal. Cliometrica 2011, 5 (3), 291–321. DOI:10.1007/s11698-011-0063-6.
Kumar, A.; Nickel, R. Reuters. February 2023.
Sources of Greenhouse Gas Emissions | US EPA. https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions (accessed 2023-10-24).
Site Considerations | US EPA. https://www.epa.gov/green-power-markets/site-considerations (accessed 2023-10-24).
How Hydropower Works. https://www.energy.gov/eere/water/how-hydropower-works (accessed 2023-10-24).
dhirajkumar612. 👨🎓Kaggle Survey 2022📄✍📊. https://www.kaggle.com/code/dhirajkumar612/kaggle-survey-2022/notebook (accessed 2023-10-24).
Friedman, J. H. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics 2001, 29 (5). DOI:10.1214/aos/1013203451.
Sohn, K.; Berthelot, D.; Carlini, N.; Zhang, Z.; Zhang, H.; Raffel, C. A.; Li, C.-L.; Kurakin, A.; Cubik, E. D. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. Advances in Neural Information Processing Systems 2020, 33, 596–608.
Kim, J.; Min, Y.; Kim, D.; Lee, G.; Seo, J.; Ryoo, K.; Kim, S. Conmatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization. Lecture Notes in Computer Science 2022, 674–690. DOI:10.1007/978-3-031-20056-4_39.
Duan, Y.; Zhao, Z.; Qi, L.; Wang, L.; Zhou, L.; Shi, Y.; Gao, Y. Mutexmatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization. IEEE Transactions on Neural Networks and Learning Systems 2022, 1–15. DOI:10.1109/tnnls.2022.3228380.
Fan, Y.; Kukleva, A.; Dai, D.; Schiele, B. Revisiting Consistency Regularization for Semi-Supervised Learning. International Journal of Computer Vision 2022, 131 (3), 626–643. DOI:10.1007/s11263-022-01723-4.
https://nsrdb.nrel.gov/data-sets/us-data (accessed 2023-10-24).
Hall, I. J. In Generation of a typical meteorological year; Sandia Laboratories, 1978.
Kalogirou, S. A. Environmental Characteristics. Solar Energy Engineering 2014, 51–123. DOI:10.1016/b978-0-12-397270-5.00002-9.
Polo, J.; Alonso-Abella, M.; Martín-Chivelet, N.; Alonso-Montesinos, J.; López, G.; Marzo, A.; Nofuentes, G.; Vela-Barrionuevo, N. Typical Meteorological Year Methodologies Applied to Solar Spectral Irradiance for PV Applications. Energy 2020, 190, 116453. DOI:10.1016/j.energy.2019.116453.
Yang, H. Study of Typical Meteorological Years and Their Effect on Building Energy and Renewable Energy Simulations. ASHRAE Transactions 2004, 110 (2), 424–431.
U.S. Climate Normals. https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-hourly&timeframe=30 (accessed 2023-10-24).
Yang, L.; Lam, J. C.; Liu, J. Analysis of Typical Meteorological Years in Different Climates of China. Energy Conversion and Management 2007, 48 (2), 654–668. DOI:10.1016/j.enconman.2006.05.016.
Climate Change Indicators: U.S. and Global Temperature. https://www.epa.gov/climate-indicators/climate-change-indicators-us-and-global-temperature (accessed 2023-10-24).
Letcher, T. M.; Scott, K. In Comprehensive renewable energy; Elsevier, 2022.
Wind Energy Maps and Data. https://windexchange.energy.gov/maps-data (accessed 2023-10-24).
Solar Resource Maps and Data. https://www.nrel.gov/gis/solar-resource-maps.html (accessed 2023-10-24).
Breiman, L. Bagging Predictors. Machine Learning 1996, 24 (2), 123–140. DOI:10.1007/bf00058655.
10. Decision Trees. https://scikit-learn.org/stable/modules/tree.html#classification (accessed 2023-10-24).
11. Ensemble Methods¶. https://scikit-learn.org/0.16/modules/ensemble.html (accessed 2023-10-24).
Arya, N. KDnuggets. 2022.
Sklearn.Ensemble.Randomforestclassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html (accessed 2023-10-24).
Breiman, L. Machine Learning 1996, 24 (1), 41–47. DOI:10.1023/a:1018094028462.
QuantDare. 2020.
Texas Land-Based Wind Speed at 100 Meters. https://windexchange.energy.gov/maps-data/357 (accessed 2023-10-24).
Global Horizontal Irradiance National Solar Radiation Data Base Physical Solar Model; 2018.
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