A Federated learning model for Electric Energy management using Blockchain Technology
Muhammad Shoaib Farooq, Azeen Ahmed Hayat

TL;DR
This paper proposes a blockchain-enabled federated learning framework for energy demand forecasting and management, integrating renewable energy sources and ensuring data privacy and security in energy transactions.
Contribution
It introduces a novel combination of blockchain technology with federated learning for secure, transparent, and efficient energy management involving prosumers and consumers.
Findings
Renewable energy sources achieve comparable results to non-renewable sources.
Blockchain ensures secure and transparent energy data transactions.
Federated learning accurately predicts energy demand.
Abstract
Energy shortfall and electricity load shedding are the main problems for developing countries. The main causes are lack of management in the energy sector and the use of non-renewable energy sources. The improved energy management and use of renewable sources can be significant to resolve energy crisis. It is necessary to increase the use of renewable energy sources (RESs) to meet the increasing energy demand due to high prices of fossil-fuel based energy. Federated learning (FL) is the most emerging technique in the field of artificial intelligence. Federated learning helps to generate global model at server side by ensemble locally trained models at remote edges sites while preserving data privacy. The global model used to predict energy demand to satisfy the needs of consumers. In this article, we have proposed Blockchain based safe distributed ledger technology for transaction of…
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Taxonomy
TopicsElectricity Theft Detection Techniques · Smart Grid Energy Management · Energy and Environment Impacts
