| Issue |
E3S Web Conf.
Volume 712, 2026
2026 16th International Conference on Future Environment and Energy (ICFEE 2026)
|
|
|---|---|---|
| Article Number | 07004 | |
| Number of page(s) | 6 | |
| Section | Data-Driven Energy Systems Management and Decision Support | |
| DOI | https://doi.org/10.1051/e3sconf/202671207004 | |
| Published online | 19 May 2026 | |
A Blockchain Framework for Trustless Next-Generation University Energy Management
1 Department of Computer Engineering, Khon Kaen University, Thailand.
2 Smart City Operation Center, Khon Kaen University, Thailand.
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Effective energy management is a critical component for achieving sustainability on university campuses. Conventional energy management systems often encounter challenges such as fragmented data silos, dispersed data ownership, and compromised data integrity due to reliance on manual and infrequent recording processes. These limitations obstruct automated data integration and impede efficient resource planning. This paper proposes a novel framework that leverages blockchain technology and smart contracts to establish a decentralized, transparent, and automated energy ledger. The system is engineered to support large-scale smart meter deployment by tokenizing energy units (kilowatt-hours) on an Ethereum Virtual Machine (EVM)-compatible blockchain. Within this framework, energy providers, including local solar farms and national utilities, can mint tokens corresponding to energy injected into the grid, while campus buildings automatically burn tokens equivalent to their energy consumed. All transactions are autonomously recorded by respective smart meters. A proof-of-concept smart contract was developed and deployed on the Thai Blockchain Services Infrastructure (TBSI). The results indicate that this approach effectively eliminates data silos, guarantees data integrity and availability through blockchain's inherent immutability, and provides a cost effective, trust minimized platform for data integration. The high frequency, granular data captured by the system establishes a robust foundation for future AI driven energy optimization and advanced microgrid management strategies.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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