ARCHIVES
Original Article
World Electricity Analysis Trends, Challenges, and Future Prospects
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.
Published Online: March-April 2025
Pages: 34-40
Cite this article
No DOIReferences
[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.
[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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