Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)
Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)
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DOI：
10.1016/j.apenergy.2016.02.141
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发表时间：
2016-05-15
期刊：
影响因子：
11.2
通讯作者：
Delgarm, S.
中科院分区：
文献类型：
--
作者：
Delgarm, N.;Sajadi, B.;Delgarm, S.
本文针对基于模拟的多目标优化问题提出了一种有效的方法，该方法解决了建筑能源性能优化方面的重要局限。在这项工作中，单目标和多目标粒子群优化（MOPSO）算法与EnergyPlus建筑能源模拟软件相结合，以找到一组非劣解来提高建筑能源性能。为了评估该方法的能力和有效性，将所开发的方法应用于一个单房间模型，并在伊朗的四个主要气候区域研究了建筑结构参数（包括建筑朝向、遮阳挑檐规格、窗户尺寸、玻璃和墙体材料特性）对建筑能耗的影响。在优化部分，对年度制冷、制热和照明用电消耗进行了单准则和多准则优化分析，以了解目标函数之间的相互作用，并使年度建筑总能源需求最小化。多目标优化过程中获得的最优解也以帕累托最优前沿的形式呈现。最后，将多准则最小化的结果与单准则的结果进行了比较。三目标优化问题的结果表明，对于我们的典型模型，与伊朗四个不同气候区域的基准模型相比，年度制冷用电量下降了约19.8 - 33.3%；而年度制热和照明用电量分别增加了1.7 - 4.8%和0.5 - 2.6%。此外，优化设计使年度建筑总用电需求减少了1.6 - 11.3%。所提出的优化方法是一种强大且有用的工具，它在寻找具有冲突目标函数的最优解时可以节省时间；因此有助于在建筑设计的早期阶段做出决策，以提高其能源效率。（C）2016 Elsevier Ltd.保留所有权利。
This paper proposes an efficient methodology for the simulation-based multi-objective optimization problems, which addresses important limitations for the optimization of the building energy performance. In this work, a mono- and multi-objective particle swarm optimization (MOPSO) algorithm is coupled with EnergyPlus building energy simulation software to find a set of non-dominated solutions to enhance the building energy performance. To evaluate the capability and effectiveness of the approach, the developed method is applied to a single room model, and the effect of building architectural parameters including, the building orientation, the shading overhang specifications, the window size, and the glazing and the wall material properties on the building energy consumption are studied in four major climatic regions of Iran. In the optimization section, mono-criterion and multi-criteria optimization analyses of the annual cooling, heating, and lighting electricity consumption are examined to understand interactions between the objective functions and to minimize the annual total building energy demand. The achieved optimum solutions from the multi-objective optimization process are also reported as Pareto optimal fronts. Finally, the result of multi-criteria minimization is compared with the mono criterion ones. The results of the triple-objective optimization problem point out that for our typical model, the annual cooling electricity decreases about 19.8-33.3%; while the annual heating and lighting ones increase 1.7-4.8% and 0.5-2.6%, respectively, in comparison to the baseline model for four diverse climatic regions of Iran. In addition, the optimum design leads to 1.6-11.3% diminution of the total annual building electricity demand. The proposed optimization method shows a powerful and useful tool that can save time while searching for the optimal solutions with conflicting objective functions; therefore facilitate decision making in early phases of a building design in order to enhance its energy efficiency. (C) 2016 Elsevier Ltd. All rights reserved.