Open Access

ARTICLE

Simulation-Based Analysis of Building Energy Consumption under Different Thermal Utilization Techniques

Xiang Ding^1,2,3, Huai Chen^1,3, Wenfeng Gao^1,2,3,*, Baihong Liu^1,3, Qiong Li^1,3
1 Solar Energy Research Institute, Yunnan Normal University, Kunming, China
2 National Quality Inspection and Testing Center for Solar Water Heating Systems, Kunming, China
3 Key Laboratory of Rural Energy Engineering of Yunnan, Kunming, China
* Corresponding Author: Wenfeng Gao. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.076058

Received 13 November 2025; Accepted 22 January 2026; Published online 25 February 2026

Abstract

Building sectors in China contributes more than 40% of the total national energy consumption, and thus efficient control of energy is important in meeting the 2-carbon target. This paper proposes QEMES–ASHP (Quantum-Enhanced Multi-Physics Energy Simulation with Air Source Heat Pump integration) as a real-time platform, combining thermal, electrical, and fluid dynamics modelling with adaptive control using AI. The main innovations of the framework include: (i) real-time multi-physics coupling, in which modelling heat–power–airflow interactions under dynamic climate conditions, (ii) machine learned system switching in solar–ASHP–PCM systems, and (iii) fractal-based time modelling, which uses a Brownian motion at fractional scales to account for energy demand fluctuations between microsecond and season scales and quantify uncertainty in a Bayesian uncertainty quantification for robust predictions. The quantum enhancement is the utilization of a variational quantum linear solver (VQLS), accelerating large-scale coupled matrices, with up to 20% faster convergence than classical solvers, and is compatible with classical HPC resources. Integration-first simulation paradigm is a name that characterizes the steady integration of various physics, predictive AI control, and uncertainty quantification into an integrated, real-time operation structure. Validation was carried out at a 200 m² IoT-instrumented test structure in 5 Chinese climate zones (hot-humid, hot-dry, temperate, cold, severe cold) and a 150+ sensor count constantly recording data over twelve months. Based on QEMES–ASHP as the reference, the findings indicate 31%–35% yearly energy savings, 15%–30% peak load saving, and PMV-based thermal comfort with the range of ±0.15, which is better than an advanced façade, radiant cooling, and thermal system with TES (p < 0.001). Computational speed was 20 percent faster, with the economic analysis paying back in 4.2 years. Life-cycle analysis suggests a 30%–40% decrease in the number of CO₂ emissions, which will be directly part of the carbon neutrality in China in 2060. QEMES–ASHP illustrates a simulation methodology that is both holistic and replicable, providing practical recommendations to architects, HVAC engineers, and policymakers who design next-generation net-zero smart buildings in a variety of Chinese climates.

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Keywords

QEMES–ASHP (Quantum-enhanced multi-physics energy simulation with air source heat pump integration); building energy optimization; multi-physics coupling; AI-driven thermal control; hybrid solar–ASHP–PCM systems; IoT-enabled smart buildings; fractal temporal modelling; Bayesian uncertainty quantification

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