Sustainable Nanomaterial-Based technologies for renewable energy production and efficient storage based on Machine learning techniques

Abstract

This research proposes novel technique in nanomaterial based renewable energy production and efficient storage based on machine learning techniques. The renewable energy production and storage has been carried out using heuristic smart grid based energy storage system with gradient boosting auto-encoder. Since the simple machine learning (ML) approach is only capable of analysing simple raw data, it cannot perform the learning process. The experimental analysis has been carried out in terms of the Root mean square error (RMSE), accuracy, energy storage capacity, electricity cost, performance and accountability reporting (PAR) and carbon emission. The proposed technique attained RMSE of 63%, accuracy of 99%, energy storage capacity of 94%, electricity cost of 56%, PAR of 58, carbon emission of 39% which will improve the renewable energy production and storage using heuristic smart grid based energy storage system with gradient boosting auto-encoder.