A Data-Driven Platform for Predicting the Position of Future Wind Turbines
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Optimal location of wind turbines is a complex decision problem involving environmental, performance, societal and other parameter. This paper investigates the domain by describing WindturbinesPlanner: by providing machine learning models trained on various data sources, the platform can help to anticipate the potential location of future onshore wind turbines in Luxembourg, France, Belgium and Germany.
KeywordsWind turbines Predictive analytics Visualisation
This work was carried out as part of the FEDER Data Analytics Platform project (http://tiny.cc/feder-dap-project). Special thanks to Anne Hendrick for her support.
- 2.Chalapathy, R., Menon, A.K., Chawla, S.: Anomaly detection using one-class neural networks. arXiv preprint arXiv:1802.06360 (2018)
- 4.Grady, S., Hussaini, M., Abdullah, M.M.: Placement of wind turbines using genetic algorithms. Renew. Energy 30(2), 259–270 (2005)Google Scholar
- 5.Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Exp. Syst. Appl. 73, 220–239 (2017)Google Scholar
- 6.Hevia-Koch, P., Ladenburg, J.: Where should wind energy be located? A review of preferences and visualisation approaches for wind turbine locations. Energy Res. Soc. Sci. 53, 23–33 (2019)Google Scholar
- 7.Ladenburg, J., Hevia-Koch, P., Petrovic, S., Knapp, L.: The offshore-onshore conundrum: preferences for wind energy considering spatial data in Denmark. Renew. Sustain. Energy Rev. 121, 109711 (2020)Google Scholar
- 8.Li, K.L., Huang, H.K., Tian, S.F., Xu, W.: Improving one-class SVM for anomaly detection. In: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693), vol. 5, pp. 3077–3081. IEEE (2003)Google Scholar
- 9.Miller, A., Li, R.: A geospatial approach for prioritizing wind farm development in Northeast Nebraska. ISPRS Int. J. Geo-inf. 3(3), 968–979 (2014)Google Scholar
- 10.Mosetti, G., Poloni, C., Diviacco, B.: Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J. Wind Eng. Ind. Aerodyn. 51(1), 105–116 (1994)Google Scholar
- 11.Wang, Y.: Deck.gl: Large-scale web-based visual analytics made easy. arXiv preprint arXiv:1910.08865 (2019)
- 12.Yang, K., Kwak, G., Cho, K., Huh, J.: Wind farm layout optimization for wake effect uniformity. Energy 183, 983–995 (2019)Google Scholar