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Analysis of Renewable Energy Policies through Decision Trees

Author

Listed:
  • Dania Ortiz

    (MIT Portugal Program, Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal)

  • Vera Migueis

    (Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), 4200-465 Porto, Portugal)

  • Vitor Leal

    (Department of Mechanical Engineering, Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal)

  • Janelle Knox-Hayes

    (Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Avenue 9-424, Cambridge, MA 02139, USA)

  • Jungwoo Chun

    (Department of Urban Studies and Planning, Massachusetts Institute of Technology, 105 Massachusetts Avenue 9-366, Cambridge, MA 02139, USA)

Abstract

This paper presents an alternative way of making predictions on the effectiveness and efficacy of Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, the analysis uses the Renewable Energy (RE) target achievement, predicting whether or not a RE target will likely be achieved (efficacy) and to what degree (effectiveness), depending on the different criteria, including geographical context, characterizing concerns, and policy characteristics. The results suggest different criteria that could help policymakers in designing policies with a higher propensity to achieve the desired goal. Using this tool, the policy decision-makers can better test/predict whether the target will be achieved and to what degree. The novelty in the present paper is the application of Machine Learning methods (through the Decision Trees) for energy policy analysis. Machine learning methodologies present an alternative way to pilot RE policies before spending lots of time, money, and other resources. We also find that using Machine Learning techniques underscores the importance of data availability. A general summary for policymakers has been included.

Suggested Citation

  • Dania Ortiz & Vera Migueis & Vitor Leal & Janelle Knox-Hayes & Jungwoo Chun, 2022. "Analysis of Renewable Energy Policies through Decision Trees," Sustainability, MDPI, vol. 14(13), pages 1-31, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7720-:d:846906
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    References listed on IDEAS

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    1. Ramit Debnath & Sarah Darby & Ronita Bardhan & Kamiar Mohaddes & Minna Sunikka-Blank, 2020. "Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research," Working Papers EPRG2019, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    2. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    3. Kamali, Sadegh & Amraee, Turaj, 2017. "Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment," Applied Energy, Elsevier, vol. 187(C), pages 50-61.
    4. Dania Ortiz & Vítor Leal, 2020. "Energy Policy Concerns, Objectives and Indicators: A Review towards a Framework for Effectiveness Assessment," Energies, MDPI, vol. 13(24), pages 1-26, December.
    5. Heinermann, Justin & Kramer, Oliver, 2016. "Machine learning ensembles for wind power prediction," Renewable Energy, Elsevier, vol. 89(C), pages 671-679.
    6. Moutis, Panayiotis & Skarvelis-Kazakos, Spyros & Brucoli, Maria, 2016. "Decision tree aided planning and energy balancing of planned community microgrids," Applied Energy, Elsevier, vol. 161(C), pages 197-205.
    7. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    8. Zakari, Abdulrasheed & Li, Guo & Khan, Irfan & Jindal, Abhinav & Tawiah, Vincent & Alvarado, Rafael, 2022. "Are abundant energy resources and Chinese business a solution to environmental prosperity in Africa?," Energy Policy, Elsevier, vol. 163(C).
    9. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    10. Zhifei Zhang & Zijian Zhao & Doo-Seoung Yeom, 2020. "Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities," Complexity, Hindawi, vol. 2020, pages 1-11, October.
    11. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    12. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    13. Lucertini, Giulia, 2012. "Evaluating public policies : Normative models beyond cost benefit analysis," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/9265 edited by Tsoukiàs, Alexis & D'Alpaos, Chiara.
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