A Hybrid CNN-Blockchain Architecture for Real-Time Energy Prediction and Decentralized Pricing

Blockchain for smart grids integrated with Convolutional Neural Networks (CNN) to ensure real-time, accurate forecasting of energy consumption and prices. This difference is that while traditional systems either employ forecasting models independently or depend solely on blockchain technology for secure energy transactions, this research has fired both within the frame to increase operation intelligence and security in smart grids. The resulting architecture is intended to collect the historical data, such as energy usage and weather conditions, through market pricing, and inputs from this data, among other data, are processed using Convolutional Neural Networks (CNN) to extract temporal patterns and spatial correlations. All these insights from the (CNN) are then shared across stakeholders with a blockchain platform ensuring immutability, transparency, and automated trust through smart contracts in their data. The innovation it brings is decentralized, tamper-free sharing of real-time predictive analytics, which sets the stage for proactive resource allocation and flexible pricing schemes. Such a dual integration would make it possible for grid self-sufficient response patterns, reduce economic footprints, and enhance sustainability. The system is validated by running simulations externally against the database of UK-DALE. Upon running this dataset, the system achieved a high forecasting accuracy at 98.5%, describing its strength and well-described potential in outperforming conventional models in their reliability and scalability. Such an integrated process of deep learning with the decentralized ledger technology lays the foundation for next-generation energy ecosystems that would be intelligent, but furthermore also resilient, secure, and sustainable.