Machine Learning-Blockchain Based Autonomic Peer-to-Peer Energy Trading System

This paper introduces a blockchain-based P2P energy trading platform, where prosumers can trade autonomously with no central authority interference. Multiple collaborate in producing to form single provider. Clients’ power consumption is monitored using smart meter that interfaces an IoT node connected blockchain private network. The contracts, invoked on the blockchain, enable autonomous interactions between parties and govern accounts behavior within Ethereum state. decentralized platform utilizes pay-per-use billing routing, by contract. A Gated Recurrent Unit (GRU) deep learning-based model, predicts future based past data aggregated blockchain. Predictions are then used set Time of Use (ToU) ranges K-mean clustering. train GRU model shared all network, making predictions transparent verifiable. Implementing clustering contract allows ToU be independent incontestable. To secure validity uploaded consensus algorithm suggested detect fraudulent nodes along Proof Location (PoL), ensuring from expected nodes. explains proposed architecture, functioning as well implementation vivid details. Results presented terms gas transaction latency under different loads.