| 摘要: |
| 在建筑能耗的计量过程中，积累了大量的实时能耗数据.这些数据的特点是数量大、噪声大，存在缺失和测量误差等.如何分析和应用如此海量数据，是一个极具挑战性的问题.以2015年上海市大型建筑的电耗数据为研究对象，通过建立多层贝叶斯模型，对各类型大型建筑的月平均单耗、年平均单耗进行估计.该结果将可以帮助政府监管部门对建筑节能工作进行有效评价. |
| 关键词: 大型公共建筑 多层贝叶斯模型 平均单耗估计 MCMC抽样 |
| DOI：10.3969/J.ISSN.100-5137.2017.02.001 |
| 分类号: |
| 基金项目:上海市科学技术委员会科研计划项目（14DZ201902） |
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| Bayesian statistical analysis on energy for consumption of large-scale public buildings in shanghai |
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Xu Pengtao, Liu Jicai, Zheng Lu, Yue Rongxian
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College of Mathematics and Science, Shanghai Normal University, Shanghai 200234, China
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| Abstract: |
| In the process of measuring the power consumed in buildings,massive quantity of real-time energy consumption data have been accumulated.Salient features of these data include large samples,noise accumulations and the presence of measurement errors,etc.Thus,how to analyze and apply these massive data becomes a very challengeable problem.In this paper,based on the dataset which include the consumption of large-scale public buildings in Shanghai for 2015,we establish a hierarchical Bayesian model to estimate the average monthly consumption and the average annual consumption of large public-scale buildings in 2015.The results will help government regulators to conduct effective evaluation on energy saving for buildings. |
| Key words: large-scale public buildings Bayesian hierarchical model estimation of the average consumption MCMC sampling |
