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高英博,顾中煊,罗淑湘,等. 能耗预测导向的建筑能耗异常数据识别与修复[J]. 科学技术与工程, 2019, 19(35): 298-304.
gaoyingbo,et al.Abnormal Data Identifying and Repairing for Building Energy Consumption guided by Energy Consumption Prediction[J].Science Technology and Engineering,2019,19(35):298-304.
能耗预测导向的建筑能耗异常数据识别与修复
Abnormal Data Identifying and Repairing for Building Energy Consumption guided by Energy Consumption Prediction
投稿时间:2019-05-21  修订日期:2019-07-07
DOI:
中文关键词:  异常数据处理  机器学习 K-means算法 KNN算法  能耗预测
英文关键词:abnormal data processing machine learning K-means algorithm KNN algorithm energy consumption prediction
基金项目:国家重点研发计划项目“基于全过程的大数据绿色建筑管理技术研究与示范”(项目编号:2017YFC0704200)
           
作者单位
高英博 北京建筑大学
顾中煊 北京建筑技术发展有限责任公司
罗淑湘 北京建筑技术发展有限责任公司
李德英 北京建筑大学
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中文摘要:
      建筑能耗异常数据处理是对建筑能耗进行准确预测的前提。为有效处理建筑能耗异常数据,本文利用基于机器学习的K-means和KNN(K-Nearest Neighbor)算法,对上海某酒店建筑2017年7月的逐时能耗数据进行了异常识别和修复。本文通过建立长短期记忆网络模型,利用处理后的能耗数据预测了该建筑2017年8月首周的逐时能耗数据。预测结果表明,本文提出的建筑能耗异常数据识别与修复方法能准确识别并修复建筑能耗异常数据,从而显著提高后续能耗预测的效果。
英文摘要:
      Abnormal data processing is the premise of accurate prediction of building energy consumption. In order to effectively deal with the abnormal data of building energy consumption, this paper uses the K-means and KNN algorithms based on machine learning to identify and repair the hourly energy consumption data for July 2017 of a hotel building in Shanghai. By establishing a long and short term memory neural network model, this paper uses the processed energy consumption data to predict the hourly energy consumption data for the first week of August 2017. The prediction results show that the method proposed in this paper can accurately identify and repair abnormal building energy consumption data, thus significantly improving the effect of subsequent energy consumption prediction.
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