A Blockchain-Integrated AI Framework for Enhancing Energy Efficiency and Sustainability in Smart Grids
In the pursuit of energy efficiency and sustainability in smart grids, leveraging blockchain integration with artificial intelligence could provide a disruptive paradigm that offers numerous benefits. In this paper, a new framework is presented using blockchain with AI in order to provide optimal energy management and sustainable smart grid. The new model combines blockchain, which provides a secure an unchangeable log of transactions; with algorithms based on AI in order to analyse and predict patterns of electricity consumption as well as provide demand response control (the ability needed by users who want to be active prosumers) accompanied by optimization of the energy delivery chain. Blockchain enforces transparency and trust among multiple stakeholders, it helps verify data integrity across entities involved in a transaction to prevent any kind of tampering or fraud. At the same time, AI-driven insights allow real-time data to inform energy systems and lead with scheduling dynamic changes in order to prevent grid losses that would result in wasted energy. The inclusion of these technologies is what helps to solve the major problems such as energy theft, data security and also reduce inefficient use of power. Indeed, this integration helps reduce operational costs and enables a shift towards cleaner and sustainable sources of energy. Simulations and real-world use cases are performed to evaluate the effectiveness of the proposed framework in improving energy efficiency for smart grid towards more sustainability.
Authors
- S. B. Goyal (ORCID: https://orcid.org/0000-0002-8411-7630)
- Hayyan Nassar Waked (ORCID: https://orcid.org/0000-0003-3355-0060)
- Anand Singh Rajawat (ORCID: https://orcid.org/0000-0001-5940-5799)
- Kulwant Singh
Institutions
- TD Bank (US)
- Sandip Foundation (IN)
- City University Malaysia (MY)
- Malaysia University of Science and Technology (MY)
Publication Details
- Journal
- Procedia Computer Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.procs.2025.04.485
- Citations
- 1
- Primary Topic
- Smart Grid Energy Management
- Type
- article
- Field-Weighted Citation Impact
- 0.73