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Efficient Capture of Solar Energy in Romania: Approach in Territorial Profile Using Predictive Statistical Techniques

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  • Rotaru Cătălin-Laurențiu

    (Bucharest University of Economic Studies, Bucharest, Romania)

  • Timiş Diana

    (Bucharest University of Economic Studies, Bucharest, Romania)

  • Grădinaru Giani-Ionel

    (Bucharest University of Economic Studies, Bucharest, Romania Institute of National Economy-Romanian Academy, Bucharest, Romania)

Abstract

Nowadays, the renewable energy sector is an area of interest for every state. Global regulations and policies encourage the development of these technologies, given the current political context, but also environmental issues. Romania, due to its geographical position and climate, is considered a country with high potential regarding the implementation of alternative sources of renewable energy. This research presents the importance of solar energy and provides a statistical analysis on the sectors influencing the implementation of green energy. At the same time, those counties that are eligible are identified and different scenarios are created for the ineligible counties that lead to their eligibility. The research develops 3 main objectives. To begin with, it is desired to be created an overview of the indicators included in the analysis, in order to develop a detailed statistical analysis of the situation of each county of Romania. Following this extracted information, the second objective is outlined, which is to create an indicator that groups counties into counties eligible for solar energy and counties ineligible for solar energy using the K-Means Cluster-unsupervised learning algorithm. Finally, using the supervised learning algorithm - Logistic Regression, predictions will be made with the help of which those sectors of activity that can be improved in order to implement green energy will be identified.

Suggested Citation

  • Rotaru Cătălin-Laurențiu & Timiş Diana & Grădinaru Giani-Ionel, 2023. "Efficient Capture of Solar Energy in Romania: Approach in Territorial Profile Using Predictive Statistical Techniques," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1519-1533, July.
    • Handle: RePEc:vrs:poicbe:v:17:y:2023:i:1:p:1519-1533:n:6
    DOI: 10.2478/picbe-2023-0137
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    References listed on IDEAS

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    1. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
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