Blockchain and Machine Learning for Real-Time Data Validation in Peer-to-Peer Energy Trading

Using machine learning and blockchain technology in peer-to-peer energy trade has made huge strides in making sure that real-time data is correct. This new way of doing things solves the problems of data quality, trust, and security in the energy business. The purpose of this study is to find out if these technologies can work together to make the approval process better. This would make energy transfers more reliable and efficient. For this study, three main areas of interest have been picked out. As a way to build trust, blockchain technology: The autonomous and unchangeable record of blockchain technology is very important for building trust in the peer-to-peer energy trade network. It makes data gathering clear, auditable, and impossible to change, which builds trust among players. Intelligent systems for finding things that aren’t normal: One common way that machine learning methods are used is to look at data about how much energy is being used in real time. When we compare our method to common methods like statistical analysis, rule-based validation, data duplication checks, expert review, threshold-based validation, and past data comparison, we can see that it works better and faster. The suggested method guarantees an average performance of 0.85, cuts validation time to $\mathbf{1 2 0}$ milliseconds, and raises accuracy to $\mathbf{9 5. 2 \%}$ compared to the usual methods. In the end, P2P energy trade driven by blockchain, and machine learning is a strong solution to the issue of validating data in real time. It provides a safe, quick, and open way to buy and sell energy, and it greatly lowers the chances of data mistakes and scams. Our plan will change the energy business. Trust, long-term success, and money benefit everybody.