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Original Article

World Electricity Analysis Trends, Challenges, and Future Prospects

  • Paper ID 904
  • Volume 6, Issue 2
  • Pages 34-40
  • Published Online: March-April 2025
  • 7 Views
Open Access Peer Reviewed

Authors

  • Shweta Shukla1
  • Dr. Noorul Islam2
  • Avinash Kumar3
  • Vaibhav Yadav4
  • Megha Saraf5

1345 Department of Electrical Engineering, Meerut Institute of Engineering and Technology, Meerut, India. 2 Professor, Department of Electrical Engineering, Meerut Institute of Engineering and Technology, Meerut, India.

Article Information

DOI
No DOI
Published Online
March-April 2025
Pages
34-40
Article Type
Original Article
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No DOI available for cross-reference

References

[1] International Energy Agency (IEA). (2020). World Energy Outlook 2020. International Energy Agency.
[2] International Energy Agency (IEA). (2021). Renewables 2021: Analysis and Forecast to 2026. International Energy Agency.
[3] International Energy Agency, “World Energy Outlook 2023”.
[4] World Bank, “Global Electricity Access Report.”
[5] International Renewable Energy Agency, “Renewable Energy Statistics 2023.”
[6] U.S. Energy Information Administration, “Electricity Generation and Consumption Trends.”
[7] Harish V, Kumar A. A review on modeling and simulation of build-ing energy systems. Renewable Sustainable Energy Rev 2016; 56:1272–92.doi:
10.1016/j.rser.2015.12.040.
[8] Wei Y, Zhang X, Shi Y, Xia L, Pan S, Wu J, et al. A review of data-driven approachesfor prediction and classification of building energy consumption.
Renewable Sustainable Energy Rev 2018; 82:1027–47. doi: 10.1016/j.rser.2017.09.108.
[9] Yildiz B, Bilbao JI, Dore J, Sproul AB. Recent advances in the analysis of residential electricity consumption and applications of smart meter data.
Appl Energy2017; 208:402–27. doi:10.1016/j.apenergy.2017.10.014.
[10] Zhou K, Yang S. Understanding household energy consumption behavior: thecontribution of energy big data analytics. Renewable Sustainable
Energy Rev2016; 56:810–19. doi: 10.1016/j.rser.2015.12.001.
[11] Hong T, Pinson P, Fan S, Zareipour H, Troccoli A, Hyndman RJ. Probabilistic energyforecasting: global energy forecasting competition 2014 and
beyond. Int J Forecast2016;32(3):896–913. doi: 10.1016/j.ijforecast.2016.02.001.
[12] Hong T, Fan S. Probabilistic electric load forecasting: a tutorial review. Int J Forecast 2016;32(3):914–38. doi: 10.1016/j.ijforecast.2015.11.011.
[13] Kalogirou S. Artificial neural networks in renewable energy systems applications:a review. Appl Energy 2001.
[14] Kalogirou S. Applications of artifcial neural-networks for energy systems. Appl Energy 2000:17–35.
[15] Kalogirou S. Applications of artifcial neural networks in energy systems - a review.Energy Convers Manage 1998:1073–87.
[16] Hippert HS, Pedreira CE, Souza RC. Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst
2001;16(1):44–55.doi:10.1109/59.910780.
[17] Weron R. Modeling and forecasting electricity loads and prices: a statistical approach. Chichester England and Hoboken NJ: John Wiley & Sons;
2006.
[18] Liu Y, Yu S, Zhu Y, Wang D, Liu J. Modeling, planning, application and management
of energy systems for isolated areas: a review. Renewable Sustainable Energy Rev 2018; 82:460–70. doi: 10.1016/j.rser.2017.09.063.
[18] Chicco G. Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 2012;42(1):68–80.doi:
10.1016/j.energy.2011.12.031.
[19] Suganthi L, Samuel AA. Energy models for demand forecastingareview. Renewable Sustainable Energy Rev 2012;16(2):1223–40.doi:
10.1016/j.rser.2011.08.014.
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