Authors :
Sakshi Pathak; Tejas Asthana; Divleen Singh Rataul; Navjeet Kaur
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/2nswtb5s
Scribd :
https://tinyurl.com/yckb822j
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2218
Abstract :
Energy is the backbone of our society,
supporting our daily activities and driving progress. It plays
a crucial role in shaping our modern way of life. The future
of global energy consumption is influenced by many factors,
including demographics, economic dynamics, technological
developments, political actions, environmental demands
and geopolitical considerations. As the world's population
continues to grow and urbanize, the demand for energy is
increasing. At the same time, rapid technological
innovations are shaping the energy landscape and changing
production, distribution and consumption patterns. In the
midst of this development, it is very important to optimize
energy consumption, accurately anticipate needs, curb
climate change, limit emissions of greenhouse gasses, fight
against pollution and promote sustainability. This study
includes an in-depth analysis of historical consumption
trends, assessing the multiple benefits of renewable energy
integration, estimating carbon emissions, formulating
practical policy recommendations and providing
empirically informed insights. The work is based on various
data obtained from platforms such as Kaggle and using
advanced visualization techniques such as Power BI
dashboards. The study provides invaluable perspectives on
future energy needs, the penetration of renewable sources
into the energy mix, and the strategic needs to achieve
sustainable energy use.
Keywords :
Energy Consumption, Demand Forecast, Sustainable Development, Analysis, Renewable Energy, Carbon Emissions, Kaggle, Power BI, Future Energy Requirements, Policy Recommendations.
References :
- K. Albrich, W. Rammer, and R. Seidl, "Climate change causes critical transitions and irreversible alterations of mountain forests," Global Change Biology, vol. 26, no. 7, pp. 4013–4027, 2020.
- "Data science in energy consumption analysis: a review of AI techniques in identifying patterns and efficiency opportunities," Engineering Science & Technology Journal, vol. 4, no. 6, pp. 357-380, Dec. 2023.
- A. M. York, C. D. Otten, S. Burnsilver, S. L. Neuberg, and J. M. Anderies, "Integrating institutional approaches and decision science to address climate change: a multi-level collective action research agenda," Curr. Opin. Environ. Sustain., vol. 52, pp. 19–26, 2021.
- F. F. Adedoyin, F. V. Bekun, and A. D. Alola, "Growth impact of transition from non-renewable to renewable energy in the EU: The role of research and development expenditure," Renewable Energy, vol. 159, pp. 1139-1145, 2020.
- N. Mostafa, H. S. M. Ramadan, and O. Elfarouk, "Renewable energy management in smart grids by using big data analytics and machine learning," Machine Learning With Applications, vol. 9, p. 100363, 2022.
- Z. Yao et al., "Machine learning for a sustainable energy future," Nature Reviews Materials, vol. 8, no. 3, pp. 202–215, 2022.
- O. Neumann et al., "Using weather data in energy time series forecasting: the benefit of input data transformations," Energy Informatics, vol. 6, no. 1, 2023.
- G. Calvo and A. Valero, "Strategic mineral resources: Availability and future estimations for the renewable energy sector," Environmental Development, vol. 41, p. 100640, 2022.
- R. Morse et al., "Can wind and solar replace coal in Texas?," Renewables, vol. 9, no. 1, p. 1, 2022.
- E. E. Michaelides, "Decarbonization of the electricity generation sector and its effects on sustainability goals," Sustainable Energy Research, vol. 10, no. 10, 2023.
- E. H. Y. Moa and Y. I. Go, "Large-scale energy storage system: safety ad risk assessment," Sustainable Energy Research, vol. 10, no. 13, 2023.
- A. Hamdan et al., "Predicting future global temperature and greenhouse gas emissions via LSTM model," Sustainable Energy Research, vol. 10, no. 21, 2023.
- T. Mollick, G. Hashmi, and S. R. Sabuj, "Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants," Sustainable Energy Research, vol. 11, no. 5, 2024.
- V. Loganathan et al., "A Case Study on Renewable Energy Sources, Power Demand, and Policies in the States of South India—Development of a Thermoelectric Model," Sustainability, vol. 14, no. 14, p. 8882, 2022.
- B. G. Desai, "Case studies for integration of renewable energy sources in power grid -- lessons for India," Current Science, vol. 120, no. 12, pp. 1827, 2021.
- P. Bouchard et al., "A Case Study on Smart Grid Technologies with Renewable Energy for Central Parts of Hamburg," Sustainability, vol. 15, no. 22, p. 15834, 2023.
- G. Alkhayat and R. Mehmood, "A Review and Taxonomy of Wind and Solar Energy Forecasting Methods Based on Deep Learning," Energy and AI, 2021.
- S. Almaghrabi et al., "Solar power time series forecasting utilizing wavelet coefficients," Neurocomputing, vol. 508, pp. 182-207, 2022.
- Y. Luiset al., "Artificial intelligence-based methods for renewable power system operation," Nat Rev Electr Eng, vol. 1, pp. 163–179, 2024.
- B. O. Abisoye, Y. Sun, and Z. W. Zenghui, "A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights," Renewable Energy Focus, 2023.
- Y. Zhang et al., "Regional carbon emission pressure and corporate green innovation," Applied Energy, vol. 360, p. 122625, 2024.
- B. Wang et al., "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, vol. 186, p. 114010, 2024.
Energy is the backbone of our society,
supporting our daily activities and driving progress. It plays
a crucial role in shaping our modern way of life. The future
of global energy consumption is influenced by many factors,
including demographics, economic dynamics, technological
developments, political actions, environmental demands
and geopolitical considerations. As the world's population
continues to grow and urbanize, the demand for energy is
increasing. At the same time, rapid technological
innovations are shaping the energy landscape and changing
production, distribution and consumption patterns. In the
midst of this development, it is very important to optimize
energy consumption, accurately anticipate needs, curb
climate change, limit emissions of greenhouse gasses, fight
against pollution and promote sustainability. This study
includes an in-depth analysis of historical consumption
trends, assessing the multiple benefits of renewable energy
integration, estimating carbon emissions, formulating
practical policy recommendations and providing
empirically informed insights. The work is based on various
data obtained from platforms such as Kaggle and using
advanced visualization techniques such as Power BI
dashboards. The study provides invaluable perspectives on
future energy needs, the penetration of renewable sources
into the energy mix, and the strategic needs to achieve
sustainable energy use.
Keywords :
Energy Consumption, Demand Forecast, Sustainable Development, Analysis, Renewable Energy, Carbon Emissions, Kaggle, Power BI, Future Energy Requirements, Policy Recommendations.