Hybrid electricity management system for residential power block applications

Developing countries have been facing electricity crisis for decades, mainly due to insufficient electricity generating capacity as compared to its demand and extensive growth in population. This research outlines Electric_Fed, a decentralized electricity management model that incorporates blockchain and federated learning (FL). This model allows multiple clients, defined as houses or blocks, to privately and efficiently collaborate on electricity consumption and production forecasting without needing to exchange raw, shared data. The model achieves collaboration on a global model. Automated electricity trading is made possible through the use of Blockchain and smart contracts, which allows clients to securely and transparently participate in real-time peer-to-peer energy exchange. In this article, electricity production model using cross device FL technique and blockchain has been presented. In the proposed model, FL has been used to provide a secured client server model to facilitate clients having surplus electricity or facing electricity shortfall within the network. Central Server issues alert to the clients having surplus electricity to start Smart contract with the client facing electricity shortfall implementing blockchain. Blockchain ensures safe transaction and reliable transfer of payments to the client selling electricity. Electricity production and consumption dataset from Jan 2024 to Dec 2024 has been processed. Experimental validation proved that the proposed model is capable enough to fulfill 89.2% electricity need of the selected region. The experimental setup results (from June 2024) validate the model's effectiveness. Client A reached a surplus of + 909 kWh, Client B + 12 kWh, Client C + 230 kWh and Client D fell short by - 693 kWh. The system automatically recognized surpluses and deficits, trading energy to balance the needs of Clients A and C, which allowed them to reach full self-sufficiency. This model lowered national grid dependency and reduced overall electricity costs. As compared to other models presented for electricity supply, our proposed model is not only more effective in fulfilling the regional electricity demands but it also suggested a secured mechanism to compensate the electricity selling client participating in electricity trade. This demonstrates, in and of itself, the effectiveness of the model proposed while providing intelligent decentralized energy management in a system that is, as a whole, efficient, scalable, and privacy preserving.