Can quantum computing surpass classical algorithms in optimizing building performance? A benchmark study with 15,000 simulations

doi:10.1016/j.enbuild.2025.116156

2025-07-15

Haidar Hosamo

Vagelis Plevris

Dimitrios Kraniotis

Christian Nordahl Rolfsen

DOI：10.1016/j.enbuild.2025.116156

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摘要

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• 量子与经典算法被用于性能优化的基准测试。 • 一个代理模型基于15,000次IDA ICE模拟进行训练以进行预测。 • NSGA-II在能耗与热舒适度之间实现了最佳权衡。 • 量子方法能提供更快的计算结果，但解的质量有所降低。 • 量子计算展现出未来实时建筑优化的潜力。

Keyword:

Quantum computing

Building performance optimization

Machine Learning

Non-dominated Sorting Genetic Algorithm II (NSGA-II)

Multi-Objective Particle Swarm Optimization (MOPSO)

期刊

Energy and Buildings

IF:

7.1

论文数:

1.6W

被引数:

6.8W

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