بهبود مدیریت عملکرد و نگهداری ساختمان های بزرگ
ABSTRACT
I. INTRODUCTION
II. THE DATACUBE FOR O&M DATA OF LARGE PUBLIC BUILDINGS
III. A DATA MINING APPROACH BASED ON THE DATACUBE
IV. A CASE STUDY
V. FINAL REMARKS
REFERENCES
ABSTRACT
With the development of modern information technologies and more frequent utilization of information systems to operation and maintenance (O&M) management, a great amount of O&M data are collected nowadays. However, because of the large volume and poor quality, as well as a lack of effective data analysis techniques, these data are rarely analyzed and translated into useful knowledge for O&M decisions. This study presents a data model, which is named as datacube with multi-dimensional and unrestrained characteristics, for these data to better support data mining algorithms. The model organizes all the different data in both relational database and in the memories and is able to support analysis-requirements-oriented data extractions. Based on this datacube, an O&M data mining approach is proposed with procedures of data preparation, data clustering and data mining. The proposed datacube-based data mining approach was applied to the Kunming Chang Shui international airport terminal. More than 7 years on-site repairing data were used for data mining and the outcomes verified the model and the approach to be feasible and valuable for improving O&M management.
INTRODUCTION
Operation and maintenance (O&M) phase lasts the longest and costs the most within the building lifecycle [1]. With the development of modern information technologies and more frequent utilization of information system to facilitate facility management, a great amount of O&M data is collected nowadays. Therefore, researches have been focusing on the smoothly information handover from previous phases to the O&M stage [2], [3] and the analyzing and utilizing of O&M data [1], especially for large public buildings such as airport terminals, stadiums, convention centers, shopping malls, etc. Some data-driven artificial intelligent methods were adopted to analysis the data collected during the O&M phase. For example, some studies applied data mining methods to analysis O&M data for an air-conditioning system in an educational building located in Montreal [4], to improve the energy efficiency for an international school campus in a tropical climate and an office building in a temperate European climate [5], and to optimize the geometrical, thermophysical and heating system attributes of apartments [6]. Such kind of methods are believed to have advantages in improving energy-saving behaviors and saving over 15% of the electricity used in the building [7]. Besides energy consumption analysis, analysis of the impact of occupants’ behavior has been largely overlooked in building energy performance analysis [8].