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 data between
prosumer and consumer to ensure their transparency, traceability and security.
Furthermore, we have also proposed a Federated learning model to forecast the
energy requirements of consumer and prosumer. Moreover, Blockchain has been
used to store excess energy data from prosumer for better management of energy
between prosumer and grid. Lastly, the experiment results revealed that
renewable energy sources have produced better and comparable results to other
non-renewable energy resources.

电力短缺和负荷限制是发展中国家面临的主要问题，主要原因是能源部门管理不善和使用非可再生能源。改善能源管理并增加可再生能源的使用对解决能源危机具有重要意义。本文提出了基于区块链的安全分布式账本技术，用于保证生产者和消费者之间数据交易的透明性、可追溯性和安全性，并提出了用于预测消费者和生产者能源需求的联邦学习模型。实验结果显示，可再生能源已经产生了与其他非可再生能源资源相媲美的更好的结果。