Predicting Failures Using Neural Networks and Quantum Optimization Techniques in Concentrated Solar Power Plants: A Case Study of Morocco Noor Ouarzazate Complex

Abstract. The Noor Ouarzazate complex in Morocco is one of the biggest concentrated solar power plants in Africa and makes 580 MW of electricity. However, there have been a lot of anomalies with the plant's operations. Examples include : mirrors get dusty every day, sensors are exposed to very high and low temperatures, and molten salt eats through storage containers at 565°C. Existing machine learning approaches consume 15 to 50 watts per cycle, which makes continuous monitoring unfeasible. This setup exhibits an integration of firing neural networks with quantum algorithmic tuning. Our neuromorphic methodology evaluates data using discontinuous spike events, demonstrating 94.7% accuracy while consuming only 2.3 microjoules of power every cycle. This network utilizes 847 times less energy than alternative networks. The integration of quantum-inspired algorithms in scheduling resulted in a 32% reduction in costs and an 18% increase in equipment durability. We studied 18 months of operational logs from 7,400 heliostats to analyze the reliability of the system. It could predict failures with 91.3% accuracy 48 to 72 hours in advance. This is the first application that uses both neuromorphic computing and quantum-inspired optimization to improve renewable energy infrastructure. This can assist Morocco succeed in its goal of getting 52% of its electrical power from sources that are sustainable by twenty-first century.