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A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study

Author

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  • Konstantinos Konstas

    (School of Social Sciences, Hellenic Open University, 18 Aristotelous St., 26335 Patras, Greece)

  • Panos T. Chountalas

    (Department of Business Administration, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece)

  • Eleni A. Didaskalou

    (Department of Business Administration, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece)

  • Dimitrios A. Georgakellos

    (Department of Business Administration, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece)

Abstract

In an era of big data, organizations increasingly aim to adopt data-driven decision-making processes to enhance their performance. This paper investigates the data-driven decision-making process by developing a framework tailored for application in the energy sector. The proposed framework integrates interdisciplinary approaches to comprehensively address the “data, information, knowledge” triad, applying it to both operational and maintenance decision-making. Designed to be managerially focused rather than technically oriented, the framework aims to engage all employees, including those without technical backgrounds, enabling them to effectively contribute to the decision-making process from their respective roles. To demonstrate the practical application of the proposed framework, this paper presents a case study of an energy organization managing a wind farm project, which implemented the framework to improve its decision-making process. The case study examines how the organization identified its objectives and information needs, formulated key performance questions for each stakeholder, explicitly defined and measured the key performance indicators, employed data collection and organization methods, managed the progression from data to information to knowledge, and transformed the acquired knowledge into informed decisions. By adopting this pragmatic framework, energy organizations are anticipated to solve problems, predict trends, and discover new opportunities, thereby enhancing their efficiency and predictability.

Suggested Citation

  • Konstantinos Konstas & Panos T. Chountalas & Eleni A. Didaskalou & Dimitrios A. Georgakellos, 2023. "A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study," Energies, MDPI, vol. 16(17), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6272-:d:1227893
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    References listed on IDEAS

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    1. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    2. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
    3. Dehghani, Hamed & Vahidi, Behrooz & Hosseinian, Seyed Hossein, 2017. "Wind farms participation in electricity markets considering uncertainties," Renewable Energy, Elsevier, vol. 101(C), pages 907-918.
    4. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    5. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    6. Imre Delgado & Muhammad Fahim, 2020. "Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System," Energies, MDPI, vol. 14(1), pages 1-21, December.
    7. Michael F. Howland & John O. Dabiri, 2019. "Wind Farm Modeling with Interpretable Physics-Informed Machine Learning," Energies, MDPI, vol. 12(14), pages 1-21, July.
    8. Sofia Spyridonidou & Dimitra G. Vagiona, 2020. "Systematic Review of Site-Selection Processes in Onshore and Offshore Wind Energy Research," Energies, MDPI, vol. 13(22), pages 1-26, November.
    9. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2010. "Power optimization of wind turbines with data mining and evolutionary computation," Renewable Energy, Elsevier, vol. 35(3), pages 695-702.
    10. Meng Li & Shuangxin Wang, 2019. "Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks," Energies, MDPI, vol. 12(17), pages 1-20, August.
    11. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    12. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    13. Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia & Santos J. González-Rojí, 2019. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula," Sustainability, MDPI, vol. 11(13), pages 1-22, July.
    14. Acikgoz, Hakan & Budak, Umit & Korkmaz, Deniz & Yildiz, Ceyhun, 2021. "WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network," Energy, Elsevier, vol. 233(C).
    15. Sun, Gaiping & Jiang, Chuanwen & Cheng, Pan & Liu, Yangyang & Wang, Xu & Fu, Yang & He, Yang, 2018. "Short-term wind power forecasts by a synthetical similar time series data mining method," Renewable Energy, Elsevier, vol. 115(C), pages 575-584.
    16. Jose V. Taboada & Vicente Diaz-Casas & Xi Yu, 2021. "Reliability and Maintenance Management Analysis on OffShore Wind Turbines (OWTs)," Energies, MDPI, vol. 14(22), pages 1-14, November.
    17. Tautz-Weinert, Jannis & Yürüşen, Nurseda Y. & Melero, Julio J. & Watson, Simon J., 2019. "Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis," Renewable Energy, Elsevier, vol. 132(C), pages 93-105.
    18. Ye, Lin & Zhao, Yongning & Zeng, Cheng & Zhang, Cihang, 2017. "Short-term wind power prediction based on spatial model," Renewable Energy, Elsevier, vol. 101(C), pages 1067-1074.
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