References
- Accenture (2016) "Optimizing Grid Performance through Advanced Operations".
- Wilcox, T., Jin, N., Flach, P., & Thumim, J. (2019). A Big Data platform for smart meter data analytics. Computers in Industry, 105, 250-259. https://doi.org/10.1016/j.compind.2018.12.010
- Alahakoon, D., & Yu, X. (2015). Smart electricity meter data intelligence for future energy systems: A survey. IEEE Transactions on Industrial Informatics, 12(1), 425-436. https://doi.org/10.1109/TII.2015.2414355
- Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215-225. https://doi.org/10.1016/j.rser.2015.11.050
- Ku, T. Y., Park, W. K., & Choi, H. (2018, July). Demand response operation method on energy big data platform. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 823-825). IEEE.
- Guilan, W., Guoliang, Z., Hongshan, Z., & Hongyang, L. (2016). Real-time big data technologies of energy internet platform. In 2016 IEEE International Conference on Power System Technology (POWERCON) (pp. 1-6). IEEE.
- Capgemini (2012) "Smart Analytics for the Utility Sector".
- Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M., Cheung, K., Tonkoski, R., ... & Manic, M. (2019). Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions. IET Smart Grid, 2(2), 141-154. https://doi.org/10.1049/iet-stg.2018.0261
- IS Group. (2012). Managing big data for smart grids and smart meters. IBM Corporation, whitepaper (May 2012).
- Fotopoulou, E., Zafeiropoulos, A., Terroso-Saenz, F., Simsek, U., Gonzalez-Vidal, A., Tsiolis, G., ... & Skarmeta, A. (2017). Providing personalized energy management and awareness services for energy efficiency in smart buildings. Sensors, 17(9), 2054. https://doi.org/10.3390/s17092054
- Jiang, R., Lu, R., Wang, Y., Luo, J., Shen, C., & Shen, X. (2014). Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Science and Technology, 19(2), 105-120. https://doi.org/10.1109/TST.2014.6787363
- Ostroff, C., & Schmitt, N. (1993). Configurations of organizational effectiveness and efficiency. Academy of management Journal, 36(6), 1345-1361. https://doi.org/10.2307/256814
- IDC (2016) "Big Data: Turning Promise Into Reality".
- Tole, A. A. (2013). Big data challenges. Database systems journal, 4(3), 31-40.
- Stein, B., & Morrison, A. (2014). The enterprise data lake: Better integration and deeper analytics. PwC Technology Forecast: Rethinking integration, 1(1-9), 18.
- Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. Ieee Access, 6, 3585-3593. https://doi.org/10.1109/ACCESS.2018.2793265
- Marinakis, V. (2020). Big Data for Energy Management and Energy-Efficient Buildings. Energies, 13(7), 1555. https://doi.org/10.3390/en13071555
- Hofmann, E. (2017). Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108-5126. https://doi.org/10.1080/00207543.2015.1061222
- Mittal, A. (2013). Trustworthiness of big data. International Journal of Computer Applications, 80(9).
- Mao, R., Xu, H., Wu, W., Li, J., Li, Y., & Lu, M. (2015). Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems. IEEE Communications Magazine, 53(1), 42-47. https://doi.org/10.1109/MCOM.2015.7010514
- Al-Salim, A. M., Lawey, A. Q., El-Gorashi, T. E., & Elmirghani, J. M. (2017). Energy efficient big data networks: Impact of volume and variety. IEEE Transactions on Network and Service Management, 15(1), 458-474. https://doi.org/10.1109/tnsm.2017.2787624
- Capgemini (2017) "The deciding factor: Big data & decision making," Capgemini Reports, 1-24.
- Kadadi, A., Agrawal, R., Nyamful, C., & Atiq, R. (2014, October). Challenges of data integration and interoperability in big data. In 2014 IEEE international conference on big data (big data) (pp. 38-40). IEEE.
- DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information systems research, 3(1), 60-95. https://doi.org/10.1287/isre.3.1.60
- Li, E. Y. (1997). Perceived importance of information system success factors: A meta analysis of group differences. Information & management, 32(1), 15-28. https://doi.org/10.1016/S0378-7206(97)00005-0
- Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
- Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A. H. (2018). An extension of Delone and McLean IS success model with self-efficacy. The International Journal of Information and Learning Technology.
- Kahn, B. K., Strong, D. M., & Wang, R. Y. (2002). Information quality benchmarks: product and service performance. Communications of the ACM, 45(4), 184-192. https://doi.org/10.1145/505248.506007
- Wang, R. Y., & Strong, D. M. (1996) Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems 12(4), pp 5-33. https://doi.org/10.1080/07421222.1996.11518099
- Fanning, K. (2016). Big Data and KPIs: A Valuable Connection. Journal of Corporate Accounting & Finance, 27(3), 17-19. https://doi.org/10.1002/jcaf.22137
- McShea, C., Oakley, D., & Mazzei, C. (2016). The reason so many analytics efforts fall short. Harvard Business Review.
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