【作者】 李郡；

【导师】 张国强； 俞准；

【作者基本信息】 湖南大学 ， 供热、供燃气、通风及空调工程， 2016， 硕士

【摘要】 建筑能耗基准评价是指通过确定建筑能耗基准值来评估建筑用能水平及相应节能潜力,并在此基础上提供可行而有效的节能建议,在当前建筑节能减排的大背景下越来越受到国内外研究者的重视。由于不同种类建筑(如住宅建筑和办公建筑)特征不一且建筑能耗影响因素众多,如何在针对特定种类建筑进行基准评价时,同时而全面地考虑其能耗影响因素是建立有效能耗基准评价方法的关键。以住宅建筑为例,一条可行的思路是首先采集建筑相关数据并建立数据库,再基于能耗影响因素对多个住宅建筑进行细化分类,以保证同类住宅建筑之间具有较高的相似度,最后基于实际能耗数据分别确定各类建筑能耗基准值及进行用能评价。然而,现有细化分类方法具有无法同时全面考虑影响因素和分类阈值主观性强等局限性,难以满足建筑能耗评价需求,有必要对其进行深入研究。本文运用数据挖掘技术,针对住宅建筑提出一种能耗基准及用能评价新方法。该方法以基于灰色关联和聚类分析的建筑住户分类模型为核心,通过灰色关联分析确定不同影响因素(即特征参数)与建筑能耗的关联度,将该关联度作为影响因素权值并结合聚类分析对建筑进行合理分类。在此基础上采用累积频率分布法确定每类建筑的能耗基准值,并进一步对建筑住户进行用能评价,提出节能建议。为验证该方法的可行性,本文将其应用于日本建筑学会所建立的住宅建筑能耗数据库并建立了基准评价模型。该模型以12个能耗影响因素作为聚类参数,将数据库中建筑住户分为4个聚类,并确定相应的能耗基准值分别为:391MJ/m2/年,425MJ/m2/年,327MJ/m2/年和390MJ/m2/年。在评估住户节能潜力上,本文选择第1类住户中的某住户为例,通过将其能耗与相应能耗基准值进行比较,得出其年节能潜力值为157MJ/m2/年。为进一步提供具体节能建议,将该住户与同类中特征最相似的节能住户进行对比分析,提出该住户在采取节能措施时应优先考虑:第一,通过节能改造提高围护结构保温隔热性能及门窗气密性;第二,通过行为节能降低采暖空调和生活热水能耗。本文所建住宅建筑能耗基准评价方法能够同时而全面地考虑建筑能耗影响因素,合理确定建筑分类阈值,从而更加准确地评价住宅建筑能耗水平并提供可行而有效的节能建议。该方法可进一步推广应用于其它种类建筑如办公建筑和酒店建筑。考虑到用户行为对建筑能耗有显著影响,而其对能耗基准值的影响大小目前仍不明确,未来的重点研究方向应为建立基于用户行为的建筑能耗基准及用能评价方法。更多还原

【Abstract】 Building energy benchmarking refers to evaluate the energy performance and corresponding energy-saving potential of a given building, thus providing feasible and effective energy-efficient strategies. In the context of the building energy saving and emission reduction, nowadays the research on this field has attracted much attention. Due to the inconsistent characteristics of buildings with different types(such as residential buildings and office buildings) and the complexity of various influencing factors, a key question on building energy benchmarking for a certain type of buildings is how to fully consider the impacts of its influencing factors simultaneously when evaluating energy-use performance. Take residential buildings as an example, a feasible solution can be described as follows: firstly, collecting energy-related data of a number of selected buildings and establishing corresponding database; then, classifying these buildings into different groups based on their influencing factors so that buildings in a same group bears striking similarities; lastly, identifying benchmarks for each group and evaluate the energy-use performance. To date different methods have been proposed for building energy benchmarking. However, these methods hardly can take various influencing factors into consideration simultaneously while the threshold for classification are highly subjective.In this paper, a new methodology for residential building energy benchmarking and energy-use evaluation is proposed based on data mining techniques. Grey relation analysis and cluster analysis are employed to establish a building classification model. Grey relational analysis is used to analyze different influencing factors(i.e. characterized parameters) of total building energy consumption. The grey relational grade is used as the weights of corresponding factors. Based on the weighted parameters, cluster analysis is performed to classify buildings into different groups. Then, accumulative analysis is conducted to identify the benchmarking values for each group. Finally, by comparison with benchmarks and other similar energy-efficient buildings, energy-saving potential and useful recommendations could be provided.To demonstrate the applicability of this methodology, it was applied to the database developed by The Architecture Institute of Japan with the main goal of establishing a benchmarking system. In this system, twelve influencing factors were selected as parameters for cluster analysis and all the buildings were classified into four groups based on these parameters. Corresponding benchmarking values for them were 391MJ/m2, 425MJ/m2, 327MJ/m2 and 390MJ/m2 respectively. To evaluate the energy-saving potential of buildings, a certain building in cluster 1(Building A) is selected as an example. Compared with the benchmarks in this cluster, the energy-saving potential of this building is identified as 157MJ/m2. Furthermore, in order to provide specific energy-saving strategies, it is compared with the energy-efficient building with the most similar characteristics in the same cluster. It is recommended that building owners should give top priority to the following suggestions: firstly, improve the performance of building insulation and air tightness of windows and doors through energy retrofitting; secondly, modify energy-use behavior to reduce energy consumption associated with HVAC and HWS.The proposed method is able to consider different influencing factors simultaneously and to identify the threshold for classification rationally. Therefore, energy-use performance can be assessed more accurately and feasible and effective energy-saving strategies can be provided. This method could be further applied to other types of buildings such as office buildings and hotel buildings. In addition, occupant behavior is widely considered as significant influencing factor of building energy consumption, while its impacts on building energy benchmarks is still unclear. Thus, the main focus of future research should be placed on establishing new building energy benchmarking and energy use evaluation methods with occupant behavior being considered.更多还原