Forecasting the steam mass flow in a powerplant using the parallel hybrid network
Abstract
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 feedforward neural network specifically designed for timeseries 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 realworld challenges faced by the energy sector, ultimately leading to optimized power plant operations.
 Publication:

arXiv eprints
 Pub Date:
 July 2023
 DOI:
 10.48550/arXiv.2307.09483
 arXiv:
 arXiv:2307.09483
 Bibcode:
 2023arXiv230709483K
 Keywords:

 Computer Science  Machine Learning;
 Computer Science  Software Engineering;
 Physics  Data Analysis;
 Statistics and Probability;
 Quantum Physics
 EPrint:
 11 pages, 5 figures