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.

本研究利用一种并行混合神经网络架构，在工业环境中设计的，结合参数化量子电路和传统前馈神经网络的时间序列预测，以提高对未来 15 分钟的蒸汽质量流量预测。我们的结果表明，并行混合模型在训练后在测试集上的均方误差损失低于纯经典模型和纯量子模型的 5.7 倍和 4.9 倍，并且在测试集上，混合模型与真实情况的相对误差更小，比纯经典模型好 2 倍。这些发现有助于更广泛地了解如何将量子和经典机器学习技术整合到能源领域所面临的实际挑战中，最终优化发电厂的运营。