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.

翻译：能源短缺与电力负荷削减是发展中国家面临的主要问题，其根源在于能源领域缺乏有效管理以及非可再生能源的使用。优化能源管理并利用可再生能源对于解决能源危机具有重要意义。鉴于化石燃料能源价格高企，必须增加可再生能源的利用以满足日益增长的能源需求。联邦学习作为人工智能领域最新兴的技术，可通过在远程边缘端集成局部训练模型，在保护数据隐私的前提下在服务器端生成全局模型。该全局模型可用于预测能源需求以满足消费者需要。本文提出一种基于区块链的安全分布式账本技术，用于产消者与消费者之间的数据交易，确保交易的透明度、可追溯性与安全性。同时，我们提出了一种联邦学习模型来预测消费者与产消者的能源需求。此外，利用区块链存储产消者的过剩能源数据，以优化产消者与电网之间的能源管理。实验结果表明，可再生能源相较其他非可再生能源产生了更优且可比的结果。