Effective distribution of energy and its utilization plays a substantial role in the energy grid, particularly with renewable energy sources. The energy grid having certain problems in utilization of resources, confidentiality in information and communication, and handling of lively demand. Hence an energy grid with blockchain and machine learning techniques is considered to solve the aforementioned problems. The grid architecture controls energy flow according to the demand, forecasts expected load, analyses consumers’ power usage, and permits the users in the energy trade via peer-to-peer manner. Energy flow in storage modules controlled with high voltage relays, and it systematizes the charging and discharging of respective batteries in the grid. Consumption of energy and battery status in the grid is uploaded to the distributed file system. Then the data clustering model deployed in the server analyses those data and divides the consumers into three groups according to their consumption behaviour: high, moderate, and low consumption.
The Time series analysis model deployed to forecast the load and predict peak hours. The codes deployed as a smart contract in an Ethereum blockchain platform and the Machine learning algorithms are deployed for forecasting and clustering. In forecasting, the average error rate is 37% less than other generally used algorithms, and in the clustering algorithm, the accuracy increases as the dataset increases, which is 30% more than other cluster models. The controlled energy storage model in this grid provides up to 500-600 extra charge cycles for batteries than other traditional methods. The distributed IPFS storage provides data security, and smart contracts support grid operational security and data privacy. The data analyzation module of the grid helps effective resource utilization.