Journal of Engineering, Project, and Production Management, 2026, 16(1), 2025-298
Design of Integrated Energy Systems for Green Buildings and Analysis of Low-Carbon Benefits
Engineer, Jilin University of Architecture and Technology, Changchun 130114, Jilin, China, E-mail: 3315420@163.com
Project Management
Received December 2, 2025; revised January 27, 2026; accepted January 28, 2026
Available online January 28, 2026
Abstract: With the increasing demand for energy conservation and low-carbon development in green buildings, the optimization design of integrated energy systems for green buildings has become a research hotspot. This paper proposes a system optimization method based on the Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D) to simultaneously optimize economic efficiency, environmental performance, and energy utilization efficiency. The study first constructs an optimization model that incorporates three objectives: investment cost, carbon emissions, and the renewable energy utilization rate. Decision variables, such as equipment capacity, operational parameters, and energy allocation ratios, are defined. Based on this model, the MOEA/D algorithm is applied to optimize a typical building case, with comparisons against Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. The study constructs a static simulation scenario based on typical meteorological year data, focusing on system performance under standard operating conditions, and does not currently account for uncertainties related to extreme weather or real-time price fluctuations. Results indicate that the proposed method achieves superior metrics compared to the comparison algorithms in terms of total system cost, carbon emissions, and energy utilization: The MOEA/D-optimized system achieves a total cost of 1.2×10⁶ yuan, representing reductions of 14.3% and 20% compared to NSGA-II and MOPSO, respectively. Carbon emissions are reduced to 150 tons, a decrease of 17%–25% over the comparison methods. Renewable energy utilization reached 60%, representing a 10%–15% improvement over other algorithms. Additionally, MOEA/D demonstrated superior convergence speed and balanced Pareto solution distribution. The study concludes that this method effectively achieves low-carbon, high-efficiency operation of green building integrated energy systems, providing a feasible pathway and technical support for building energy conservation, emission reduction, and sustainable development.
Keywords: Multi-objective optimization algorithm, green building integrated energy system, low-carbon benefit analysis, decomposable multi-objective evolutionary algorithm.
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Citation: Yuan, Y. (2026). Design of Integrated Energy Systems for Green Buildings and Analysis of Low-Carbon Benefits. Journal of Engineering, Project, and Production Management, 16(1), 2025-298.
DOI:
10.32738/JEPPM-2025-298
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