Forecasting steam mass flow in power plants using the parallel hybrid network

TL;DR

This paper introduces a novel parallel hybrid quantum-classical neural network for predicting steam mass flow in power plants, demonstrating significant improvements over classical and quantum models in real-world industrial data.

Contribution

It presents the first deployment of a parallel hybrid quantum-classical neural network on a power-plant dataset, enhancing prediction accuracy for industrial time-series data.

Findings

Hybrid model outperforms classical and quantum models in mean squared error

Hybrid model achieves over 5.7 times lower MSE than classical model

Hybrid model shows up to 2 times smaller prediction errors

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 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 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. To our knowledge, this study constitutes the first parallel hybrid quantum-classical architecture deployed on a real-world power-plant dataset, illustrating how near-term quantum resources can already augment classical analytics in the energy sector.