Efficient Quantum-Enhanced Ensemble Fault Detection for Solar Energy Integration using an Iterative Game-Theoretic Approach with Adaptive Neuro-Fuzzy Inference and Energy Storage

The rising energy requirements across the globe coupled with the need for sustainable development makes it imperative to harness renewable sources such as solar energy. That being said, changes in the weather and instability in the systems can enhance the reliability and efficiency of solar power systems which in the case of the current systems poses quite a challenge. The current solar power systems cannot adapt to such changes therefore causing energy wastages as well as problems with the grid. The new model presented here, the Ensemble Fault Detection Model for Solar Deployments (EFDMSD), uses technology to resolve a number of issues. This model has been built to harvest solar energy with greater speed and accuracy by means of Quantum Machine Learning (QML). A system’s changes can be addressed appropriately in real-time, thanks to the model’s Adaptive Neuro-Fuzzy Inference Control’s (ANFIS) control system. In other words, Game Theory is applied to explain energy shortage scenarios better and manage supply for peak periods. Energy Storage Systems are also present within the mix which allows for excess solar energy to be accumulated making the supply of energy more secure. These techniques, as a result of using artificial intelligence, are able to enhance energy production, stabilize grids, and optimize performance for many hardware configurations. With the implementation of the EFDMSD model, the availability and reliability of energy through the use of simpler solar systems is enhanced. Hence, there would be changes in the economic impacts while reducing the grid impact thus leading the country towards clean energy.