Abstract
This study delves into the intricate relationship between power plant attributes and electricity generation, employing data analysis and predictive modelling techniques. Through a comprehensive analysis of a global power plant dataset, critical factors such as plant capacity and commissioning year were identified as significant influencers on electricity generation. The research utilized correlation heatmaps to visually represent these relationships, offering valuable insights for policymakers and investors. A linear regression model was employed, leveraging capacity and commissioning year as features to predict electricity generation. The model's accuracy was evaluated using mean squared error, providing a quantitative measure of its predictive capabilities.
References
N. Munier, “Transition to Renewable Energy: An Attempt to Model the Mix of Existing and Future Generation Technologies for 2035 and 2050,” Biophysical Economics and Sustainability, vol. 8, no. 4, p. 6, 2023, doi: 10.1007/s41247-023-00114-8.
M. Imran, U. Zaman, Imran, J. Imtiaz, M. Fayaz, and J. Gwak, “Comprehensive survey of iot, machine learning, and blockchain for health care applications: A topical assessment for pandemic preparedness, challenges, and solutions,” Electronics (Switzerland), vol. 10, no. 20. MDPI, Oct. 01, 2021. doi: 10.3390/electronics10202501.
B. S. Neyigapula, “Synergistic Integration of Blockchain and Machine Learning: Advancements, Applications, and Challenges.”[Online]. Available: https://ssrn.com/abstract=4538519
A. Qazi et al., “Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions,” IEEE Access, vol. 7, pp. 63837–63851, 2019, doi: 10.1109/ACCESS.2019.2906402.
M. J. B. Kabeyi and O. A. Olanrewaju, “Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply,” Frontiers in Energy Research, vol. 9. Frontiers Media S.A., Mar. 24, 2022. doi: 10.3389/fenrg.2021.743114.
M. Zhou, “Exploring Application of Machine Learning to Power System Analysis.”[Online]. Available: www.interpss.org.
A. Kumbhar, P. G. Dhawale, S. Kumbhar, U. Patil, and P. Magdum, “A comprehensive review: Machine learning and its application in integrated power system,” Energy Reports, vol. 7, pp. 5467–5474, 2021, doi: https://doi.org/10.1016/j.egyr.2021.08.133.
R. Vaish, U. D. Dwivedi, S. Tewari, and S. M. Tripathi, “Machine learning applications in power system fault diagnosis: Research advancements and perspectives,” Eng Appl Artif Intell, vol. 106, p. 104504, 2021, doi: https://doi.org/10.1016/j.engappai.2021.104504.
A. Entezari, A. Aslani, R. Zahedi, and Y. Noorollahi, “Artificial intelligence and machine learning in energy systems: A bibliographic perspective,” Energy Strategy Reviews, vol. 45, p. 101017, 2023, doi: https://doi.org/10.1016/j.esr.2022.101017.
L. Wehenkel, “Machine-learning approaches to power-system security assessment,” IEEE Expert-Intelligent Systems and their Applications, vol. 12, no. 5, pp. 60–72, Sep. 1997, doi: 10.1109/64.621229.
F. Chen, H. Wan, H. Cai, and G. Cheng, “Machine Learning in/for Blockchain: Future and Challenges,” Sep. 2019,[Online]. Available: http://arxiv.org/abs/1909.06189
“ENERGYDATA.INFO.”
S. Zhao, Y. Guo, Q. Sheng, and Y. Shyr, “Advanced Heat Map and Clustering Analysis Using Heatmap3,” Biomed Res Int, vol. 2014, 2014, doi: 10.1155/2014/986048.
B. C. M. (Benno) Haarman, R. F. Riemersma-Van der Lek, W. A. Nolen, R. Mendes, H. A. Drexhage, and H. Burger, “Feature-expression heat maps – A new visual method to explore complex associations between two variable sets,” J Biomed Inform, vol. 53, pp. 156–161, 2015, doi: https://doi.org/10.1016/j.jbi.2014.10.003.
S. Islam, “Impact investing in social sector organisations: a systematic review and research agenda,” Accounting and Finance, vol. 62, pp. 709–737, Jan. 2022, doi: 10.1111/acfi.12804.
R. K. F. Bresser and C. Powalla, “Practical implications of the resource-based view,” Zeitschrift für Betriebswirtschaft, vol. 82, no. 4, pp. 335–359, 2012, doi: 10.1007/s11573-012-0553-4.