MACHINE LEARNING APPLICATIONS FOR OPTIMISING QUANTUM NANOTECHNOLOGY IN RENEWABLE ENERGY PRODUCTION AND SUSTAINABLE GREEN SYSTEMS

Renewable energy generation and encouraging environmentally friendly green systems can be revolutionised by merging with automated learning and quantum nanotechnology. The reduction of energy consumption, system reliability prediction, and architecture enhancement of quantum dot-based solar energy systems are the primary goals of the present study, which uses automated learning methods, namely LSTM Neural Networks. Machine learning algorithms allow for precise energy production and system operation prediction by assessing material qualities, ambient conditions, and time-lapse energy information. Learning through reinforcement is a valuable tool to further improve resource generating procedures in real-time, with adaptive management and decreased energy losses. The investigation shows that solutions based on information significantly increase yield optimisation while enhancing energy consumption estimates by 15-20%. Furthermore, conventional energy sources may be made more sustainable in the long run by including nanoscale components, which reduce power loss. This research investigation highlights the possibility of solving energy problems, encouraging creative thinking, and adding to a cleaner, more effective worldwide energy system by integrating quantum nanotechnology with sophisticated machine learning techniques.