SeeQ: A Programming Model for Portable Data-driven Building Applications

被引:2
|
作者
Mavrokapnidis, Dimitris [1 ]
Fierro, Gabe [2 ]
Husmann, Maria [3 ]
Korolija, Ivan [1 ]
Rovas, Dimitrios [1 ]
机构
[1] UCL, London, England
[2] Natl Renewable Energy Lab, Colorado Sch Mines, Golden, CO USA
[3] Siemens AG, Zug, Switzerland
关键词
Programming; Analytics; Portability; Scalability; Brick; RDF; SHACL; Metadata; Semantic Web; Ontologies;
D O I
10.1145/3600100.3623744
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper introduces SeeQ, a programming model and an abstraction framework that facilitates the development of portable datadriven building applications. Data-driven approaches can provide insights into building operations and guide decision-making to achieve operational objectives. Yet the configuration of such applications per building requires extensive effort and tacit knowledge. In SeeQ, we propose a portable programming model and build a software system that enables self-configuration and execution across diverse buildings. The configuration of each building is captured in a unified data model - in this paper, we work with the Brick ontology without loss of generality. SeeQ focuses on the distinction between the application logic and the configuration of an application against building-specific data inputs and systems. We test the proposed approach by configuring and deploying a diverse range of applications across five heterogeneous real-world buildings. The analysis shows the potential of SeeQ to significantly reduce the efforts associated with the delivery of building analytics.
引用
收藏
页码:159 / 168
页数:10
相关论文
共 50 条
  • [31] Data-Driven Materials Innovation and Applications
    Wang, Zhuo
    Sun, Zhehao
    Yin, Hang
    Liu, Xinghui
    Wang, Jinlan
    Zhao, Haitao
    Pang, Cheng Heng
    Wu, Tao
    Li, Shuzhou
    Yin, Zongyou
    Yu, Xue-Feng
    ADVANCED MATERIALS, 2022, 34 (36)
  • [32] Data-driven building archetypes for urban building energy modelling
    Pasichnyi, Oleksii
    Wallin, Jorgen
    Kordas, Olga
    ENERGY, 2019, 181 : 360 - 377
  • [33] A data-driven reflectance model
    Matusik, W
    Pfister, H
    Brand, M
    McMillan, L
    ACM TRANSACTIONS ON GRAPHICS, 2003, 22 (03): : 759 - 769
  • [34] Data-driven estimation of building interior plans
    Rosser, Julian F.
    Smith, Gavin
    Morley, Jeremy G.
    INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2017, 31 (08) : 1652 - 1674
  • [35] On the causality of data-driven building thermal models
    Jiang, Fuyang
    Driesen, Johan
    Kazmi, Hussain
    PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023, 2023, : 454 - 457
  • [36] Data-driven and adaptive control applications to a wind turbine benchmark model
    Simani, Silvio
    Castaldi, Paolo
    CONTROL ENGINEERING PRACTICE, 2013, 21 (12) : 1678 - 1693
  • [37] A Big Data-driven Digital Twin Model Method for Building a Shop Floor
    Yan, Jihong
    Ji, Siyang
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2023, 59 (12): : 63 - 77
  • [38] A Data-Driven Predictive Model for Speed Control in Automotive Safety Applications
    Samsudeen, S.
    Kumar, G. Senthil
    IEEE SENSORS JOURNAL, 2022, 22 (23) : 23258 - 23266
  • [39] Building the optimal hybrid spatial Data-Driven Model: Balancing accuracy and complexity
    Barca, Emanuele
    Caputo, Maria Clementina
    Masciale, Rita
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2025, 139
  • [40] A data-driven model to determine the infiltration characteristics of air curtains at building entrances
    Song, Linye
    Zhang, Cong
    Hua, Jing
    Li, Kaijun
    Xu, Wei
    Zhang, Xinghui
    Duan, Chengchuan
    PHYSICS OF FLUIDS, 2023, 35 (11)