Forecasting the steam mass flow in a powerplant using the parallel
  hybrid network
Andrii Kurkin Jonas Hegemann Mo Kordzanganeh Alexey Melnikov
Efficient and sustainable power generation is a crucial concern in the energy
sector. In particular, thermal power plants grapple with accurately predicting
steam mass flow, which is crucial for operational efficiency and cost
reduction. In this study, we use a parallel hybrid neural network architecture
that combines a parametrized quantum circuit and a conventional feed-forward
neural network specifically designed for time-series prediction in industrial
settings to enhance predictions of steam mass flow 15 minutes into the future.
Our results show that the parallel hybrid model outperforms standalone
classical and quantum models, achieving more than 5.7 and 4.9 times lower mean
squared error (MSE) loss on the test set after training compared to pure
classical and pure quantum networks, respectively. Furthermore, the hybrid
model demonstrates smaller relative errors between the ground truth and the
model predictions on the test set, up to 2 times better than the pure classical
model. These findings contribute to the broader scientific understanding of how
integrating quantum and classical machine learning techniques can be applied to
real-world challenges faced by the energy sector, ultimately leading to
optimized power plant operations.