信息可视化辅助决策系统在健康建筑中的应用
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摘要
基于信息可视化技术,并利用数据仓库、联机分析处理、数据挖掘等核心技术,可将大量数据进行提取、整理、分析,为决策者进行经营决策提供支持。基于信息可视化的健康建筑管理平台从不同的角度抽取反应建筑室内环境的各类指标,集成建筑使用中所产生的各类数据,基于全面、系统、实时的利用各类数据,建立相关的数据模型,让建筑业主和住户实现实时自行监测,从真正意义上实现实时在线监控。用户可通过应用此平台加强对自身的管理,对以后的发展方向做出及时、科学的决策。
Huge account of building performance data are generated,collected and preserved in Building Automation Systems( BASs),which have been widely implemented with the development of smart building technology. Based on the key information which under covered in these data, building performance management plan can be created and improved. As a technology complex which concludes data warehouse,on-line analysis and data mining,information visualization can extract,collate,and analyze large amounts of data to support decision-makers in making business decisions. A business intelligence-based health building management platform introduced in this paper can collect and store various indoor air environment quality( IEQ) and water quality data into a database. After processing with a predefined procedure,all processed data can be displayed with a user-friendly graphic interface.With the assistant of this system,building operating managers can make IEQ management decision much more quickly according to real-time online monitoring data.
Huge account of building performance data are generated,collected and preserved in Building Automation Systems( BASs),which have been widely implemented with the development of smart building technology. Based on the key information which under covered in these data, building performance management plan can be created and improved. As a technology complex which concludes data warehouse,on-line analysis and data mining,information visualization can extract,collate,and analyze large amounts of data to support decision-makers in making business decisions. A business intelligence-based health building management platform introduced in this paper can collect and store various indoor air environment quality( IEQ) and water quality data into a database. After processing with a predefined procedure,all processed data can be displayed with a user-friendly graphic interface.With the assistant of this system,building operating managers can make IEQ management decision much more quickly according to real-time online monitoring data.
引文
[1]黄健恒.大型办公建筑能耗与室内环境质量无线监测及评价系统研究[D].西安:西安建筑科技大学,2013.
[2]孙世光.油田勘探开发业务模型可视化技术研究[D].大庆:东北石油大学,2013.
[3]James J.Thomas,Kristin A.Cook,Illuminating the Path:The R&D agenda for visual analytics[M].National Visualization and Analytics Center,2005:30.
[4]Stuart K.Card,Jock Mackinlay,Ben Shneiderman.Readings in information visualization:using vision to think(interactive technologies)[M].S.l.:s.n.,1999.
[5]P.Raghunathan.Data analysis and decision making:using spreadsheets and pivot tables to get aread on energy numbers[J].Information Technology for Energy M anagers,The Fairmont Press,Inc.,2004:157-169.
[6]S.Tom.Introduction to web-based information and control systems[J].Information Technology for Energy M anagers,The Fairmont Press,Inc.,2004:9-15.
[7]Dale Fong,Allan Schurr.Relational database choices and design[J].Information Technology for Energy M anagers,The Fairmont Press,Inc.,2004:255-263.
[8]MIHALAKAKOU G,SANTAMOURIS M,TSANGRASSOULIS A.On the energy consumption in residential buildings[J].Energy and Buildings,2002,34(7):727-736.
[9]Xudong Ma,Ran Cui,Yu Sun,etal.Supervisory and energy management system of large public buildings[C]//2010 IEEE International Conference on M echatronics and Automation,ICM A 2010(EI):928-933.
[10]R.Kimball,M.Ross,W.Thornthwaite,J.Mundy,B.Becker.The Data Warehouse Lifecycle Toolkit:Practical Techniques for Building Data Warehouse and Business Intelligence Systems[M].John Wiley&Sons,2008:59.
[11]Geoffrey K.F.Tso,Kelvin K.W.Yau.Predicting electricity energy consumption:a comparison of regression analysis,decision tree and neural netw orks[J].Energy,2007,32(9):1761-1768.
[2]孙世光.油田勘探开发业务模型可视化技术研究[D].大庆:东北石油大学,2013.
[3]James J.Thomas,Kristin A.Cook,Illuminating the Path:The R&D agenda for visual analytics[M].National Visualization and Analytics Center,2005:30.
[4]Stuart K.Card,Jock Mackinlay,Ben Shneiderman.Readings in information visualization:using vision to think(interactive technologies)[M].S.l.:s.n.,1999.
[5]P.Raghunathan.Data analysis and decision making:using spreadsheets and pivot tables to get aread on energy numbers[J].Information Technology for Energy M anagers,The Fairmont Press,Inc.,2004:157-169.
[6]S.Tom.Introduction to web-based information and control systems[J].Information Technology for Energy M anagers,The Fairmont Press,Inc.,2004:9-15.
[7]Dale Fong,Allan Schurr.Relational database choices and design[J].Information Technology for Energy M anagers,The Fairmont Press,Inc.,2004:255-263.
[8]MIHALAKAKOU G,SANTAMOURIS M,TSANGRASSOULIS A.On the energy consumption in residential buildings[J].Energy and Buildings,2002,34(7):727-736.
[9]Xudong Ma,Ran Cui,Yu Sun,etal.Supervisory and energy management system of large public buildings[C]//2010 IEEE International Conference on M echatronics and Automation,ICM A 2010(EI):928-933.
[10]R.Kimball,M.Ross,W.Thornthwaite,J.Mundy,B.Becker.The Data Warehouse Lifecycle Toolkit:Practical Techniques for Building Data Warehouse and Business Intelligence Systems[M].John Wiley&Sons,2008:59.
[11]Geoffrey K.F.Tso,Kelvin K.W.Yau.Predicting electricity energy consumption:a comparison of regression analysis,decision tree and neural netw orks[J].Energy,2007,32(9):1761-1768.