Modeling energy-related CO2 emissions from office buildings using general regression neural network
Modeling energy-related CO2 emissions from office buildings using general regression neural network
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使用通用回归神经网络对办公楼与能源相关的二氧化碳排放进行建模
DOI：
10.1016/j.resconrec.2017.10.020
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Li Xinhu
中科院分区：
文献类型：
--
作者：
Ye Hong;Ren Qun;Hu Xinyue;Lin Tao;Shi Longyu;Zhang Guoqin;Li Xinhu
城市办公建筑能源消耗（BEC）产生的二氧化碳（CO2）排放是人为温室气体排放的重要组成部分，并将随着城市化的进一步发展而迅速增加。建立一个简洁、准确、现实的模型来预测未来的排放量是一项具有挑战性的工作，但对于制定城市低碳建设和可持续发展战略至关重要。本文对全国294座办公建筑的运行能耗进行了统计分析。我们专注于四个主要变量，并进一步分析了10个二级变量，阐明建筑物的居住者的作用，其结构特征，和本地化的自然条件在确定能源消耗。利用广义回归神经网络（GRNN），检验了各因素对能耗的直接和间接影响。建筑物的结构属性对能源相关的CO2排放影响最大，其次是相关的社会经济条件，小气候，最后是区域气候。一个版本的模型，构建了四个主要变量之间的相互作用被认为是最精确的。将GRNN与城市发展情景相结合，对城市未来CO2排放量进行了预测。经济发展和建筑业标准的提高可能对未来的二氧化碳排放量产生重大影响。这项研究提供了一种详细的方法，可用于探索办公室能源使用的动态和低碳办公楼建设的竞争选择。
Carbon dioxide (CO2) emissions from urban office buildings energy usages (BEC) constitute a substantial component of anthropogenic greenhouse gas emission, and are set to rapidly increase with further urbanization. Establishing a concise, accurate, and realistic model that can predict future emissions is challenging but essential for strategies to develop low carbon construction and sustainable development in urban areas. In this paper, the operational energy use for 294 office buildings across China was collected and analyzed. We focus on four main variables, and analyze a further ten second-level variables, to elucidate the role that a building’s occupants, its structural characteristics, and localized natural conditions play in determining energy consumption. Using general regression neural network (GRNN), the factors’ direct and indirect effects on energy consumption were tested. A building’s structural attributes had the most impact on energy-related CO2emissions, followed by the relevant socioeconomic conditions, the micro-climate, and finally the regional climate. A version of the model that was constructed with interaction between the four main variables was found to be the most precise. GRNN combined with urban development scenarios was used for the prediction of cities’ future CO2emissions. Economic development and improving standards in the construction industry could have significant impacts on future CO2emissions. This study provides a detailed method that could be used to explore the dynamics of office energy use and competing options for the construction of low-carbon office buildings.