Verifying energy generation via edge LLM for web3-based decentralized clean energy networks

The global transition to clean energy is critical to achieving climate goals, yet traditional centralized systems face challenges in flexibility, grid resilience, and equitable access. While decentralized web3-based energy networks offer promising alternatives, existing solutions lack robust architectures to integrate distributed generation with real-time demand and fail to provide trustworthy energy verification mechanisms. This work introduces DeCEN, a decentralized clean energy network that synergizes collaborative edge computing and web3 technologies to address these gaps. DeCEN leverages autonomous edge devices to collect and process sensory data from renewable generators, enabling localized decision-making and verification of energy production. A layer-2 blockchain solution establishes a transparent web3 ecosystem, connecting clean energy generators and consumers through tokenized incentives for green energy activities. To combat fraud, DeCEN incorporates a novel large language model (LLM)-based energy verification protocol that analyzes sensory data to validate renewable claims, ensuring accountability and stabilizing token value. Additionally, a distributed LLM inference algorithm partitions LLMs into shards deployable on resource-constrained edge devices, enabling decentralized, low-latency processing while preserving data privacy and minimizing communication overhead. By integrating edge computing, blockchain, and AI-driven verification, DeCEN improves the reliability, trust, and efficiency of decentralized clean energy networks, offering a scalable pathway toward global renewable energy targets.