基于径向基函数神经网络的严寒地区建筑能耗与舒适度优化研究

贺龙; 吕保; 马德宇

doi:10.13204/j.gyjzG21111518

基于径向基函数神经网络的严寒地区建筑能耗与舒适度优化研究

doi: 10.13204/j.gyjzG21111518

内蒙古工业大学建筑学院, 呼和浩特 010051

基金项目:

国家自然科学基金项目（51968054）；内蒙古自治区科技计划项目（2021GG0249）。

详细信息

作者简介:
贺龙,男,1984年出生,博士,教授。电子信箱:helongjianzhu@163.com

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- 文章访问数: 98
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- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-15
- 网络出版日期: 2023-12-18

Research on Multi-Objective Optimization of Building Energy Efficiency and Comfort Based on RBF Neural Network

College of Architecture, Inner Mongolia University of Technology, Hohhot 010051, China

摘要

摘要: 建筑形态作为建筑方案设计阶段需要重点考虑的设计因素，对建筑的多项性能指标有直接的影响。以严寒地区不同子气候区的气象参数为研究背景，将建筑形态分解为8个量化因子，以建筑能耗和基于ANSI/ASHRAE 55-2004《人类居住热环境条件》评价标准的建筑全年不舒适时间为优化指标，基于EnergyPlus仿真模拟平台获取评价指标的原始数据，引入径向基函数（RBF）神经网络建立影响因子与优化指标之间的快速反应模型，结合正交试验法分别对各研究地区的中小型办公建筑能耗与全年不舒适时间进行了单目标寻优计算，分析了严寒地区3个代表城市中小型办公建筑的形态因子对于不同优化指标的影响权重以及优化潜力，并进一步探究了各研究地区形态因子基于节能与舒适的最佳组合方案与最不利组合方案。结果表明：建筑形态的量化设计参数对于降低建筑能耗以及建筑的全年不舒适时间均有比较可观的优化潜力。
- 建筑形态 /
- 建筑节能 /
- 热舒适 /
- RBF神经网络
Abstract: Building morphology, as a design factor to be focused on in the design phase of building scheme, has a direct impact on several performance indicators of the building. The meteorological parameters of different sub-climatic zones in severe cold regions were used as the research background, and the building form was decomposed into 8 quantitative factors, and the building energy consumption and the annual discomfort time of the building based on Thermal Environmental Conditions for Human Occupancy(ANSI/ASHRAE 55-2004) evaluation standard were used as the optimization indexes. Based on the EnergyPlus simulation platform, the original data of the evaluation indexes was obtained, the radial basis function neural network was introduced to establish the rapid response model between the influencing factors and the optimization indexes, combined with the orthogonal test method, the single-objective optimization calculation of the building energy consumption and the annual discomfort time in each study area were carried out, and the influence weight and optimization potential of the three morphological factors representing urban buildings in severe cold regions on different optimization indexes were analyzed. The optimal combination of energy saving and comfort based on morphological factors in each study area was further investigated. The results showed that the quantitative design parameters of building morphology had considerable optimization potential for reducing building energy consumption and building discomfort time throughout the year.
- building form /
- building energy efficiency /
- thermal comfort /
- RBF neural network

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