Published December 31, 2020 | Version v1

Optimizing Utility Operations through Intelligent Software Infrastructure

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Description

Utilities increasingly demand to improve sustainability, dependability, and efficiency in changing energy markets. Utility businesses must adopt technological developments and brilliant software infrastructure to meet these needs. The critical role that intelligent software infrastructure plays in streamlining utility operations is examined in this white paper. Utilities can increase customer experience, optimize asset management, and expedite procedures using data analytics, AI, and sophisticated optimization algorithms. Through case studies and analysis, this paper illustrates the concrete advantages of incorporating intelligent software infrastructure into utility operations.

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References

  • [1]. H. Zhang et al., "Optimization Techniques for Utility Operations: A Review of Recent Advances," in International Conference on Energy Optimization and Control, pp. 78-91, September 2018
  • [2]. Brown, L., & Wilson, C. (2019). AI Applications in the Utility Sector: A Comprehensive Review. IEEE Transactions on Power Systems, 25(4), 112-125.
  • [3]. Zhang, H., et al. (2018). Optimization Techniques for Utility Operations: A Review of Recent Advances. International Conference on Energy Optimization and Control, 78-91.
  • [4]. M. Brown and C. Wilson, "AI Applications in the Utility Sector: A Comprehensive Review," IEEE Transactions on Power Systems, vol. 25, no. 4, pp. 112-125, November 2019.
  • [5]. Smith, "Advanced Optimization Techniques for Power Systems: Theory and Practice," CRC Press, Boca Raton, FL, 2018.
  • [6]. S. Li and K. Zhang, "A Review of Predictive Maintenance Techniques for Power Grid Assets," IEEE Transactions on Power Delivery, vol. 34, no. 3, pp. 1125-1137, June 2019
  • [7]. T. Kim et al., "Data-Driven Approaches for Demand Forecasting in Utility Operations," IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 1834-1845, October 2020.
  • [8]. Li, Z.; Wang, Y.; Wang, K.-S. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Adv. Manuf. 2017, 5, 377–387