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Energy consumption optimisation through machine learning: a strategic approach to sustainable procurement in smart grids and buildings: a bibliometric analysis

Author

Listed:
  • Saravanan Thirunavukkarasu
  • K. Kavitha
  • T. Sandeep
  • P. Shalini Reddy
  • R. Madhavi
  • L. Dillipriya

Abstract

This paper addresses whether machine learning, optimising energy intake in smart grids and smart buildings, improves efficiency, stability, and sustainability of energy use. A bibliometric analysis was performed to identify the principal contributors, thematic patterns, and research gaps within the domain. Techniques of artificial neural networks and reinforcement learning support real-time decisions by integrating renewable sources in maintaining a stable energy supply. Our results underscore the disruptive potential of ML in reducing wasted energy, shifting the shape of demand, and achieving more effective, cost-effective, resilient grids for energy. It unravels the immense promise that ML holds in transforming energy-efficient solutions worldwide.

Suggested Citation

  • Saravanan Thirunavukkarasu & K. Kavitha & T. Sandeep & P. Shalini Reddy & R. Madhavi & L. Dillipriya, 2026. "Energy consumption optimisation through machine learning: a strategic approach to sustainable procurement in smart grids and buildings: a bibliometric analysis," International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 26(2), pages 129-159.
    • Handle: RePEc:ids:ijpman:v:26:y:2026:i:2:p:129-159
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