This paper reviews eight research studies on smart grids and their role in mitigating greenhouse gas (GHG) emissions, which have become a critical concern worldwide. Renewable energy sources such as solar, wind, and hydro produce minimal or no greenhouse gases and, unlike fossil fuels, are abundant, sustainable, and ensure a long-term energy supply. Large volumes of data have been produced by recent technical developments and thorough historical data collection. Efficient data utilization in the energy sector and smart grids are crucial for providing accurate forecasts and innovative solutions. Big data analytics offers powerful tools for researchers and practitioners to analyze and interpret this information. This review provides insights into the analytical methods of big data in the renewable energy domain, enabling researchers and practitioners to identify the most suitable analysis techniques based on their specific data and research objectives.

Keywords: Big data, descriptive analysis, diagnostic analysis, predictive analysis, prescriptive analysis.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Soomro K, Bhutta MN, Khan Z, Tahir MA. Smart city big data analytics: An advanced review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2019 Sep;9(5):e1319.
2. Ma S, Huang Y, Liu Y, Kong X, Yin L, Chen G. Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries. Applied Energy. 2023 May 1; 337:120843.
3. Marinakis V. Big data for energy management and energy-efficient buildings. Energies. 2020 Mar 27;13(7):1555.
4. Taherdoost H. A systematic review of big data innovations in smart grids. Results in Engineering. 2024 Apr 21:102132.
5. Aggarwal S, Chaudhary R, Aujla GS, Kumar N, Choo KK, Zomaya AY. Blockchain for smart communities: Applications, challenges and opportunities. Journal of Network and Computer Applications. 2019 Oct 15;144:13-48.
6. Bhattarai BP, Paudyal S, Luo Y, Mohanpurkar M, Cheung K, Tonkoski R, Hovsapian R, Myers KS, Zhang R, Zhao P, Manic M. Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions. IET Smart Grid. 2019 Jun;2(2):141-54.
7. Gbadeyan OJ, Muthivhi J, Linganiso LZ, Deenadayalu N. Decoupling economic growth from carbon emissions: a transition toward low-carbon energy systems—a critical review. Clean Technologies. 2024 Aug 19;6(3):1076-113.
8. Wang Z, Li C. Building a Collaborative Data Analytics System: Opportunities and Challenges. Proceedings of the VLDB Endowment. 2023 Aug 1;16(12):3898-901.
9. Langmead B, Nellore A. Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics. 2018 Apr;19(4):208-19.
10. Yu N, Shah S, Johnson R, Sherick R, Hong M, Loparo K. Big data analytics in power distribution systems. In2015 IEEE power & energy society innovative smart grid technologies conference (ISGT) 2015 Feb 18 (pp. 1-5). IEEE.

This paper reviews eight research studies on smart grids and their role in mitigating greenhouse gas (GHG) emissions, which have become a critical concern worldwide. Renewable energy sources such as solar, wind, and hydro produce minimal or no greenhouse gases and, unlike fossil fuels, are abundant, sustainable, and ensure a long-term energy supply. Large volumes of data have been produced by recent technical developments and thorough historical data collection. Efficient data utilization in the energy sector and smart grids are crucial for providing accurate forecasts and innovative solutions. Big data analytics offers powerful tools for researchers and practitioners to analyze and interpret this information. This review provides insights into the analytical methods of big data in the renewable energy domain, enabling researchers and practitioners to identify the most suitable analysis techniques based on their specific data and research objectives.

Keywords: Big data, descriptive analysis, diagnostic analysis, predictive analysis, prescriptive analysis.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Soomro K, Bhutta MN, Khan Z, Tahir MA. Smart city big data analytics: An advanced review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2019 Sep;9(5):e1319.
2. Ma S, Huang Y, Liu Y, Kong X, Yin L, Chen G. Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries. Applied Energy. 2023 May 1; 337:120843.
3. Marinakis V. Big data for energy management and energy-efficient buildings. Energies. 2020 Mar 27;13(7):1555.
4. Taherdoost H. A systematic review of big data innovations in smart grids. Results in Engineering. 2024 Apr 21:102132.
5. Aggarwal S, Chaudhary R, Aujla GS, Kumar N, Choo KK, Zomaya AY. Blockchain for smart communities: Applications, challenges and opportunities. Journal of Network and Computer Applications. 2019 Oct 15;144:13-48.
6. Bhattarai BP, Paudyal S, Luo Y, Mohanpurkar M, Cheung K, Tonkoski R, Hovsapian R, Myers KS, Zhang R, Zhao P, Manic M. Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions. IET Smart Grid. 2019 Jun;2(2):141-54.
7. Gbadeyan OJ, Muthivhi J, Linganiso LZ, Deenadayalu N. Decoupling economic growth from carbon emissions: a transition toward low-carbon energy systems—a critical review. Clean Technologies. 2024 Aug 19;6(3):1076-113.
8. Wang Z, Li C. Building a Collaborative Data Analytics System: Opportunities and Challenges. Proceedings of the VLDB Endowment. 2023 Aug 1;16(12):3898-901.
9. Langmead B, Nellore A. Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics. 2018 Apr;19(4):208-19.
10. Yu N, Shah S, Johnson R, Sherick R, Hong M, Loparo K. Big data analytics in power distribution systems. In2015 IEEE power & energy society innovative smart grid technologies conference (ISGT) 2015 Feb 18 (pp. 1-5). IEEE.