Effective factors for residential building energy modeling using feature engineering
Effective factors for residential building energy modeling using feature engineering
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DOI：
10.1016/j.jobe.2021.102891
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发表时间：
2021-06
影响因子：
6.4
通讯作者：
Yunjeong Mo;Dong Zhao
中科院分区：
文献类型：
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作者：
Yunjeong Mo;Dong Zhao
鉴于对可持续性的理解有所提高，数百个因素被确定为与建筑节能有关。然而，仍然缺乏知识，什么因素在住宅建筑能耗预测中起着重要作用。在没有这些信息的情况下，建筑能耗预测将是没有效率的。为了解决这个问题，本研究创建了一个基于特征工程的分析框架，以选择有效的因素，能源消耗预测和评估其影响。通过两个应用实例，验证了该方法对住宅建筑能耗预测的有效性。这些案例使用了住宅能源消费调查数据库，该数据库包含了美国270多个与建筑物和居住者有关的能源使用相关因素。两个案例的数据分析表明，所选特征在使用12-15%数量的因子时达到97-102%的预测能力，大大降低了能源预测的维度。结果还产生了一个列表的重要功能，是有效的预测在国家和地区层面的住宅能源建模和评估。所选功能的示例包括房间和完整浴室的总数、使用干衣机的频率、住房单元的类型、吊扇和电视的数量。所选功能解释能源使用模式及其关系，帮助设计师，承包商和居住者更好地了解能源，行为和建筑环境。由此产生的能源使用模式告知区域的相似性，差异和独特的特点。
Given the improved understanding of sustainability, hundreds of factors are identified to have relevance to building energy efficiency. However, there is still a lack of knowledge about what factors play a significant role in energy consumption prediction for residential buildings. In the absence of this information, building energy consumption prediction would not be efficient. To tackle this problem, this study creates a feature engineering-based analytic framework to select effective factors for energy consumption prediction and assess their implications. Two application cases are reported to demonstrate the efficiency improvement of energy consumption prediction for residential buildings. The cases use the Residential Energy Consumption Survey database that contains more than 270 energy use-related factors about buildings and occupants in the United States. Data analysis from the two cases shows that selected features achieve 97–102% of prediction power while using 12–15% number of factors, largely reducing the dimensionality for energy prediction. The results also produce a list of significant features that are efficient predictors for residential energy modeling and evaluation at the national and regional levels. Examples of the selected features are the total number of rooms and full bathrooms, frequency of clothes dryer used, type of the housing unit, number of ceiling fans and television. The selected features explain energy use patterns and their relationships which help designer, contractors, and occupants better understand energy, behaviors, and the built environment. The resultant energy use patterns inform regional similarities, differences, and distinctive characteristics.