Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools

El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y...

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Autores:: Colmenares Quintero, Ramón Fernando
Quiroga Parra, Darío de Jesús
Rojas, Natalia
Stansfield, Kim E.
Colmenares Quintero, Juan Carlos

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Cooperativa de Colombia

Repositorio:: Repositorio UCC

Idioma:

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repository_id_str
dc.title.spa.fl_str_mv	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools
title	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools
spellingShingle	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools Análisis de Big Data Redes inteligentes Energía renovable Inteligencia empresarial Objetivos de Desarrollo Sostenible Big Data analytics Smart Grids Renewable energies Business Intelligence Sustainable Development Goals (SDG)
title_short	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools
title_full	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools
title_fullStr	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools
title_full_unstemmed	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools
title_sort	Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools
dc.creator.fl_str_mv	Colmenares Quintero, Ramón Fernando Quiroga Parra, Darío de Jesús Rojas, Natalia Stansfield, Kim E. Colmenares Quintero, Juan Carlos
dc.contributor.author.none.fl_str_mv	Colmenares Quintero, Ramón Fernando Quiroga Parra, Darío de Jesús Rojas, Natalia Stansfield, Kim E. Colmenares Quintero, Juan Carlos
dc.subject.spa.fl_str_mv	Análisis de Big Data Redes inteligentes Energía renovable Inteligencia empresarial Objetivos de Desarrollo Sostenible
topic	Análisis de Big Data Redes inteligentes Energía renovable Inteligencia empresarial Objetivos de Desarrollo Sostenible Big Data analytics Smart Grids Renewable energies Business Intelligence Sustainable Development Goals (SDG)
dc.subject.other.spa.fl_str_mv	Big Data analytics Smart Grids Renewable energies Business Intelligence Sustainable Development Goals (SDG)
description	El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y la tecnología. La mayoría de las fuentes de energía convencionales han demostrado ser finitas y agotables. A su vez, las diferentes actividades de producción de bienes y servicios que utilizan combustibles fósiles y energía convencional, han aumentado significativamente la contaminación del medio ambiente, y con ello, han contribuido al calentamiento global. El objetivo de este trabajo fue realizar una aproximación teórica a las tecnologías de análisis de datos e inteligencia de negocio aplicadas a las redes de sistemas eléctricos inteligentes con energías renovables. Para este trabajo se realizó una revisión bibliométrica y bibliográfica sobre Big Data Analytics, herramientas TIC de la industria 4.0 y Business intelligence en diferentes bases de datos disponibles en el dominio público. Los resultados del análisis indican la importancia del uso de la analítica de datos y la inteligencia de negocio en la gestión de las empresas energéticas. El trabajo concluye señalando cómo se está aplicando la inteligencia de negocio y la analítica de datos en ejemplos concretos de empresas energéticas y su creciente importancia en la toma de decisiones estratégicas y operativas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-07-09T16:15:02Z
dc.date.available.none.fl_str_mv	2021-07-09T16:15:02Z
dc.date.issued.none.fl_str_mv	2021-05-25
dc.type.none.fl_str_mv	Artículos Científicos
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dc.identifier.issn.spa.fl_str_mv	23311916
dc.identifier.uri.spa.fl_str_mv	https://doi.org/10.1080/23311916.2021.1935410
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12494/35013
dc.identifier.bibliographicCitation.spa.fl_str_mv	Colmenares-Quintero, R.F., Quiroga-Parra, D. J., Rojas, N., Stansfield, K.E. y Colmenares-Quintero, J.C. (2021). Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools, Cogent Engineering, 8:1, 1905232, DOI: 10.1080/23311916.2021.1935410
identifier_str_mv	23311916 Colmenares-Quintero, R.F., Quiroga-Parra, D. J., Rojas, N., Stansfield, K.E. y Colmenares-Quintero, J.C. (2021). Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools, Cogent Engineering, 8:1, 1905232, DOI: 10.1080/23311916.2021.1935410
url	https://doi.org/10.1080/23311916.2021.1935410 https://hdl.handle.net/20.500.12494/35013
dc.relation.isversionof.spa.fl_str_mv	https://www.tandfonline.com/doi/full/10.1080/23311916.2021.1935410
dc.relation.ispartofjournal.spa.fl_str_mv	Cogent Engineering
dc.relation.references.spa.fl_str_mv	Bhattarai, Bishnu P., Paudyal, Sumit, Luo, Yusheng., Mohanpurka, Manish., Cheung, Kwok., Tonkoski, Reinaldo., Hovsapian, Rob., Myers, Kurt., Zhang, Rui., Zhao, Power., Manic, Milos., Zhang, Song., Zhang, Xiaping. (2019). Big Data Analytics in Smart Grids: State-of-the-Art, Challenges, Opportunities, and Future Directions. IET Research Journals, 2, 2, 1–15. Bitlumens (2018). White paper. URL: https://bitlumens.com/assets/bitlumensdata/White_Paper_June_2018.pdf Bentov, Iddo, Gabizon, Ariel and Mizrahi, Alex. (2016). Cryptocurrencies without Proof of Work. Springer, Berlin, 142–157. DOI: http://dx.doi.org/10.1007/978-3-662-53357-4_10. Bitlumens GmbH. (2018). White Paper May 2018 - BitLumens. Recuperado de https://bitlumens.com/assets/bitlumensdata/White_Paper_June_2018.pdf. Colmenares-Quintero, R.F., Maestre-Gongora, G., Pacheco-Moreno, L.J., Rojas, N., Stansfield, K. E. & Colmenares-Quintero, J. C. (2021). Analysis of the energy service in non-interconnected zones of Colombia using business intelligence, Cogent Engineering, 8:1, 1907970, 1-21. URL: https://doi.org/10.1080/23311916.2021.1907970. Chen, Hsinchun, Chiang, Roger., and Storey, Veda (2012) Business Intelligence and Analytics from Big Data to Big Impact. MIS Quarterly, 36, 4, 1165-1188. David, P. (1990). The dynamo and the computer: a historical perspective on the modern productivity paradox. American Economic Review Papers and Proceedings,80,2,355-361. Escobedo Briones, Guillermo Flavio. and Arroyo Figueroa, Gustavo. (2016). Business Intelligence and Data Analytics (BI&DA) to Support the Operation of Smart Grid - Business Intelligence and Data Analytics (BI&DA) for Smart Grid. Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), 489-496. Escobedo Briones, Guillermo Flavio, and Arroyo-Figueroa, Gustavo. (2017). Big Data & Analytics to Support the Renewable Energy Integration of Smart Grids - Case Study: Power Solar Generation. Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), 267-275. Hagfeldt, Anders., Boschloo, Gerrit., Sun, Licheng., Kloo, Lars., and Pettersson, Henrik. (2010) Dye-Sensitized Solar Cells. Chemical Reviews, 110, 6595–6663. Hossain, E., Imtiaj Khan, I., Un-Noor,F, Sikander, S., and Sunny, M.S.H. (2018). Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review, IEEE Access, VOLUME 7, 2019, 13960- 13988. Howson, Cindi, Richardson, James., Sallam, Rita., Kronz., Austin. (2019). Magic Quadrant for Analytics and Business Intelligence Platforms. Gartner, 1-60. Grätzel, Michael. (2001). Photoelectrochemical cells. Nature, 414, 338–344 Green, M. A. (2001). Crystalline silicon photovoltaic cells. Advanced Materials,13, 1019 – 1020. Jacome Grajales, N., Escobedo Briones, G., and Guadarrama Villa, E. (2011) Inteligencia de Negocios en el área de seguridad de la CFE. Congreso Internacional sobre Innovación y Desarrollo Tecnológico, 677–685. Je, S-M., \| Huh, J‐H. (2019). Estimation of future power consumption level in smart grid: Application of fuzzy logic and genetic algorithm on big data platform Khanna, Manju., Srinath, N. K., and Mendiratta, J. K. (2015). Data Mining in Smart Grids-A Review. International Journal of Advanced Research in Computer Science and Software Engineering. 5, 3, 709-712. Ko J-S., Huh J-H, and Kim J-Ch (2020). Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid. Electronics 2020, 9, 816. Komp, R. J. (2001). Practical photovoltaics: electricity from solar cells, 3a. ed., aatec publications: Ann Arbor. Machado, C. T. and Miranda, F. S. (2016). Energía Solar Fotovoltaica: Una Breve Revisão. Rev. Virtual Quim., 2015, 7 (1), 126-143. Mendling, Jan., Weber, Ingo., Van Der Aalst, Wil., Vom Brocke, Jan., Cabanillas, Cristina., Daniel, Florian., Zhu, Liming. (2018). Blockchains for Business Process Management – Challenges and Opportunities. ACM Transactions on Management Information Systems. 9, 1, 1-15. Morais, J., Pires, and Cardoso, C. A. Klautau (2009) An overview of data mining techniques applied to power systems. In: J. Ponce, A. Karahoca (eds.) Data Mining and Knowledge Discovery in Real Life Applications. I-Tech Education and Publishing Mujeeb, S. , Javaid, N. , Akbar, M, Khalid, R. , Nazeer, O. and Khan,M. (2019). Big Data Analytics for Price and Load Forecasting in Smart Grids. BWCCA, LNDECT 25, pp. 77–87 Obeidat, Muhammad., North, Max., Richardson, Ronny., Rattanak, Vebol., North, Nakamoto, Satoshi. (2008). Bitcoin: A peer-to-peer electronic cash system. Recuperado de https://bitcoin.org/bitcoin.pdf. Nick Szabo (1997). Formalizing and securing relationships on public networks. First Monday. 2 NIST. (2010). Framework and Roadmap for Smart Grid Interoperability Standards N. S. P. 1108., Release 1.0, January. Nonaka, I., y Takeuchi, H. (1999). La organización creadora de conocimiento: cómo las compañías japonesas crean la dinámica de la innovación. México: Oxford. Okoro OI (2006). Madueme TC. Solar energy: a necessary investment in a developing economy. International Journal of Sustainable Energy, 25, 1, 23–31. Panwar, N.L., Kaushik, S.C., and Kothari, Surendra (2011). Role of renewable energy sources in environmental protection: A review. Renewable and Sustainable Energy Reviews, 15, 1513–1524. Quiroga, Darío., Hernández, Beatriz., Torrent, Joan., and Ramírez, John. (2014) La innovación de productos en las empresas, caso empresa América Latina. Revista Cuadernos del CENDES, 87, 63-85. Reddy, Paramati Sudharshan., Sinha, Avik. and Dogan, Eyup. (2017). The significance of renewable energy uses for economic output and environmental protection: evidence from the Next 11 developing economies. Environ Sci Pollut Res 24, 15, 13546–13560. Sarah. (2015). Business Intelligence Technology, Applications, and Trends. International Management Review, 11, 2, 47-55. Schiermeier, Q., Tollefson, Jeff, Scully, T., Witze, A., Morton, O. (2008). Electricity without carbon. Nature, 454, 816-823. Service, R. F. (2005). Is It Time to Shoot for the Sun? Science, 309, 548-551 Smestad, Greg P. (2002). optoelectronics of solar cells, 1a. ed., SPIE: Bellingham. Stoppato A. (2008). Life cycle assessment of photovoltaic electricity generation. Energy, 33,224-232. Torrent, J. (2004). Innovació tecnològica, creixement econòmic i economia del coneixement. Barcelona: Consell de Treball Econòmic i Social de Catalunya (CTESC), Generalitat de Catalunya. Vilaseca, J. y Torrent, J. (2006). TIC, conocimiento y crecimiento económico. Un análisis empírico, agregado e internacional sobre las fuentes de la productividad. Economía Industrial, 360, 41-60. Walport, Mark. (2016). Distributed Ledger Technology: Beyond Blockchain. UK Government Office for Science, Tech. Rep 19. Watson, Hugh, and Wixom, Barb. (2007). The current state of business intelligence, Compute, 40, 9, 96-99. Yeoh, William, and Koronios, Andy. (2010). Critical Success Factors for Business Intelligence Systems. Journal of Computer Information Systems, 50, 23-32. Zhou, Kaile, Fu, Chao., and Yang, Shanlin. (2016). Big data-driven smart energy management: From big data to big insights, Renewable and Sustainable Energy Reviews, 56, 215–225. Zhang, Y. Huang, T. and Bompard, E. F. (2018). Big data analytics in smart grids: a review. Energy Informatics 1:8, 3-24. UR: https://doi.org/10.1186/s42162-018-0007-5 Zeyar, Aung. (2013). Database Systems for the Smart Grid, Book Smart Grids: Opportunities, Developments, and Trends 151-168. London, England: Springer Verlag
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spelling	Colmenares Quintero, Ramón FernandoQuiroga Parra, Darío de JesúsRojas, NataliaStansfield, Kim E.Colmenares Quintero, Juan CarlosVol. 82021-07-09T16:15:02Z2021-07-09T16:15:02Z2021-05-2523311916https://doi.org/10.1080/23311916.2021.1935410https://hdl.handle.net/20.500.12494/35013Colmenares-Quintero, R.F., Quiroga-Parra, D. J., Rojas, N., Stansfield, K.E. y Colmenares-Quintero, J.C. (2021). Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools, Cogent Engineering, 8:1, 1905232, DOI: 10.1080/23311916.2021.1935410El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y la tecnología. La mayoría de las fuentes de energía convencionales han demostrado ser finitas y agotables. A su vez, las diferentes actividades de producción de bienes y servicios que utilizan combustibles fósiles y energía convencional, han aumentado significativamente la contaminación del medio ambiente, y con ello, han contribuido al calentamiento global. El objetivo de este trabajo fue realizar una aproximación teórica a las tecnologías de análisis de datos e inteligencia de negocio aplicadas a las redes de sistemas eléctricos inteligentes con energías renovables. Para este trabajo se realizó una revisión bibliométrica y bibliográfica sobre Big Data Analytics, herramientas TIC de la industria 4.0 y Business intelligence en diferentes bases de datos disponibles en el dominio público. Los resultados del análisis indican la importancia del uso de la analítica de datos y la inteligencia de negocio en la gestión de las empresas energéticas. El trabajo concluye señalando cómo se está aplicando la inteligencia de negocio y la analítica de datos en ejemplos concretos de empresas energéticas y su creciente importancia en la toma de decisiones estratégicas y operativasThe industrial and economic development of the industrialized countries, from the nineteenth century, has gone hand in hand with the development of electricity, the internal combustion engine, computers, the Internet, data use and the intensive use of knowledge focused on science and the technology. Most conventional energy sources have proven to be finite and exhaustible. In turn, the different production activities of goods and services using fossil fuels and conventional energy, have significantly increased the pollution of the environment, and with it, contributed to global warming. The objective of this work was to carry out a theoretical approach to data analytics and business intelligence technologies applied to smart electrical-system networks with renewable energies. For this paper, a bibliometric and bibliographic review about Big Data Analytics, ICT tools of industry 4.0 and Business intelligence was carried out in different databases available in the public domain. The results of the analysis indicate the importance of the use of data analytics and business intelligence in the management of energy companies. The paper concludes by pointing out how business intelligence and data analytics are being applied in specific examples of energy companies and their growing importance in strategic and operational decision makinghttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://orcid.org/0000-0003-1166-1982https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961ramon.colmenaresq@campusucc.edu.codario.quirogap@campusucc.edu.conatalia.rojas@aquatera.co.ukK.Stansfield@warwick.ac.ukjcarloscolmenares@ichf.edu.plhttps://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=es1- 15 p.Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Civil, Medellín y EnvigadoIngeniería mecanicaMedellínhttps://www.tandfonline.com/doi/full/10.1080/23311916.2021.1935410Cogent EngineeringBhattarai, Bishnu P., Paudyal, Sumit, Luo, Yusheng., Mohanpurka, Manish., Cheung, Kwok., Tonkoski, Reinaldo., Hovsapian, Rob., Myers, Kurt., Zhang, Rui., Zhao, Power., Manic, Milos., Zhang, Song., Zhang, Xiaping. (2019). Big Data Analytics in Smart Grids: State-of-the-Art, Challenges, Opportunities, and Future Directions. IET Research Journals, 2, 2, 1–15.Bitlumens (2018). White paper. URL: https://bitlumens.com/assets/bitlumensdata/White_Paper_June_2018.pdfBentov, Iddo, Gabizon, Ariel and Mizrahi, Alex. (2016). Cryptocurrencies without Proof of Work. Springer, Berlin, 142–157. DOI: http://dx.doi.org/10.1007/978-3-662-53357-4_10.Bitlumens GmbH. (2018). White Paper May 2018 - BitLumens. Recuperado de https://bitlumens.com/assets/bitlumensdata/White_Paper_June_2018.pdf.Colmenares-Quintero, R.F., Maestre-Gongora, G., Pacheco-Moreno, L.J., Rojas, N., Stansfield, K. E. & Colmenares-Quintero, J. C. (2021). Analysis of the energy service in non-interconnected zones of Colombia using business intelligence, Cogent Engineering, 8:1, 1907970, 1-21. URL: https://doi.org/10.1080/23311916.2021.1907970.Chen, Hsinchun, Chiang, Roger., and Storey, Veda (2012) Business Intelligence and Analytics from Big Data to Big Impact. MIS Quarterly, 36, 4, 1165-1188.David, P. (1990). The dynamo and the computer: a historical perspective on the modern productivity paradox. American Economic Review Papers and Proceedings,80,2,355-361.Escobedo Briones, Guillermo Flavio. and Arroyo Figueroa, Gustavo. (2016). Business Intelligence and Data Analytics (BI&DA) to Support the Operation of Smart Grid - Business Intelligence and Data Analytics (BI&DA) for Smart Grid. Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), 489-496.Escobedo Briones, Guillermo Flavio, and Arroyo-Figueroa, Gustavo. (2017). Big Data & Analytics to Support the Renewable Energy Integration of Smart Grids - Case Study: Power Solar Generation. Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), 267-275.Hagfeldt, Anders., Boschloo, Gerrit., Sun, Licheng., Kloo, Lars., and Pettersson, Henrik. (2010) Dye-Sensitized Solar Cells. Chemical Reviews, 110, 6595–6663.Hossain, E., Imtiaj Khan, I., Un-Noor,F, Sikander, S., and Sunny, M.S.H. (2018). Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review, IEEE Access, VOLUME 7, 2019, 13960- 13988.Howson, Cindi, Richardson, James., Sallam, Rita., Kronz., Austin. (2019). Magic Quadrant for Analytics and Business Intelligence Platforms. Gartner, 1-60.Grätzel, Michael. (2001). Photoelectrochemical cells. Nature, 414, 338–344Green, M. A. (2001). Crystalline silicon photovoltaic cells. Advanced Materials,13, 1019 – 1020.Jacome Grajales, N., Escobedo Briones, G., and Guadarrama Villa, E. (2011) Inteligencia de Negocios en el área de seguridad de la CFE. Congreso Internacional sobre Innovación y Desarrollo Tecnológico, 677–685.Je, S-M., \| Huh, J‐H. (2019). Estimation of future power consumption level in smart grid: Application of fuzzy logic and genetic algorithm on big data platformKhanna, Manju., Srinath, N. K., and Mendiratta, J. K. (2015). Data Mining in Smart Grids-A Review. International Journal of Advanced Research in Computer Science and Software Engineering. 5, 3, 709-712.Ko J-S., Huh J-H, and Kim J-Ch (2020). Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid. Electronics 2020, 9, 816.Komp, R. J. (2001). Practical photovoltaics: electricity from solar cells, 3a. ed., aatec publications: Ann Arbor.Machado, C. T. and Miranda, F. S. (2016). Energía Solar Fotovoltaica: Una Breve Revisão. Rev. Virtual Quim., 2015, 7 (1), 126-143.Mendling, Jan., Weber, Ingo., Van Der Aalst, Wil., Vom Brocke, Jan., Cabanillas, Cristina., Daniel, Florian., Zhu, Liming. (2018). Blockchains for Business Process Management – Challenges and Opportunities. ACM Transactions on Management Information Systems. 9, 1, 1-15.Morais, J., Pires, and Cardoso, C. A. Klautau (2009) An overview of data mining techniques applied to power systems. In: J. Ponce, A. Karahoca (eds.) Data Mining and Knowledge Discovery in Real Life Applications. I-Tech Education and PublishingMujeeb, S. , Javaid, N. , Akbar, M, Khalid, R. , Nazeer, O. and Khan,M. (2019). Big Data Analytics for Price and Load Forecasting in Smart Grids. BWCCA, LNDECT 25, pp. 77–87Obeidat, Muhammad., North, Max., Richardson, Ronny., Rattanak, Vebol., North, Nakamoto, Satoshi. (2008). Bitcoin: A peer-to-peer electronic cash system. Recuperado de https://bitcoin.org/bitcoin.pdf.Nick Szabo (1997). Formalizing and securing relationships on public networks. First Monday. 2 NIST. (2010). Framework and Roadmap for Smart Grid Interoperability Standards N. S. P. 1108., Release 1.0, January.Nonaka, I., y Takeuchi, H. (1999). La organización creadora de conocimiento: cómo las compañías japonesas crean la dinámica de la innovación. México: Oxford.Okoro OI (2006). Madueme TC. Solar energy: a necessary investment in a developing economy. International Journal of Sustainable Energy, 25, 1, 23–31.Panwar, N.L., Kaushik, S.C., and Kothari, Surendra (2011). Role of renewable energy sources in environmental protection: A review. Renewable and Sustainable Energy Reviews, 15, 1513–1524.Quiroga, Darío., Hernández, Beatriz., Torrent, Joan., and Ramírez, John. (2014) La innovación de productos en las empresas, caso empresa América Latina. Revista Cuadernos del CENDES, 87, 63-85.Reddy, Paramati Sudharshan., Sinha, Avik. and Dogan, Eyup. (2017). The significance of renewable energy uses for economic output and environmental protection: evidence from the Next 11 developing economies. Environ Sci Pollut Res 24, 15, 13546–13560.Sarah. (2015). Business Intelligence Technology, Applications, and Trends. 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Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools

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