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