A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses

Borui Cui, Cheng Fan, Jeffrey Munk, Ning Mao, Fu Xiao, Jin Dong and Teja Kuruganti

Applied Energy, 2019, vol. 236, issue C, 116 pages

Abstract: Within the residential building sector, the air-conditioning (AC) load is the main target for peak load shifting and reduction since it is the largest contributor to peak demand. By leveraging its power flexibility, residential AC is a good candidate to provide building demand response and peak load shifting. For realization of accurate and reliable control of AC loads, a building thermal model, which characterizes the properties of a building’s envelope and its thermal mass, is an essential component for accurate indoor temperature or cooling/heating demand prediction. Building thermal models include two types: “Forward” and “Data-Driven”. Due to time-saving and cost-effective characteristics, different data-driven models have been developed in a number of research studies. However, few developed models can predict temperatures in respective zones of a multiple-zone building with an open air path between zones e.g., an open stairwell connecting two floors of a home. In this research, a novel hybrid modeling approach is proposed to predict the average indoor air temperatures of both the upstairs and downstairs. This “hybrid” solution integrates both gray-box, i.e. RC model and black-box models. A developed RC model is used to predict the building mean temperature, and black-box model, in which the supervised machine learning algorithms are leveraged, is used to predict the temperature difference between the downstairs and upstairs. Compared with the measured data from a real house, the results obtained have acceptable/satisfactory accuracy. The method proposed in this study integrates the advantages of black-box and gray-box modeling. It can be used as a reliable alternative to predict the average temperatures in respective floors of typical detached two-story houses.

Keywords: Building demand management; Data-driven model; Supervised machine learning; Particle swarm optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:236:y:2019:i:c:p:101-116

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DOI: 10.1016/j.apenergy.2018.11.077

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