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Research Article

Data mining based framework to identify rule based operation strategies for buildings with power metering system

Shunian Qiu, Fan Feng, Zhengwei Li( ), Guang Yang, Peng Xu, Zhenhai Li
School of Mechanical Engineering, Tongji University, Shanghai, China
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Abstract

Operation strategies influence the building energy efficiency. In order to enhance the building energy efficiency, it’s necessary to adopt proper operation strategies on building equipment. Thus, the identification of existing operation strategies is necessary for the improvement of operation strategies. A data mining (DM) based framework is proposed in this paper to automatically identify the building operation strategies. The framework includes classification and regression tree (CART), and weighted association rule mining (WARM) method, targeting at three types of rule based control strategies: on/off control, sequencing control (for equipment of the same type), and coordinated control (for equipment of different types). The performance of this framework is validated with power metering system data and manual identification results based on on-site survey of three buildings in Shanghai. The validation results suggest that the proposed framework is capable of identifying building operation strategies accurately and automatically. Implemented on the original software named BOSA (Building Operation Strategy Analysis), this framework is promising to be used in engineering field to enhance the efficiency of building operation strategy identification work.

Keywords

data miningoperation strategypower metering systemautomatic identificationweighted association rule miningclassification and regression tree

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Building Simulation
Volume 12 Issue 2,
April 2019
Pages 195-205
DOI: 10.1007/s12273-018-0472-6
Cite this article:
Qiu S, Feng F, Li Z, et al. Data mining based framework to identify rule based operation strategies for buildings with power metering system. Building Simulation, 2019, 12(2): 195-205. https://doi.org/10.1007/s12273-018-0472-6

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Received: 20 January 2018
Revised: 10 August 2018
Accepted: 13 August 2018
Published: 27 September 2018
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018
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