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An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

1
School of Technology and Business Studies, Dalarna University, 79188 Falun, Sweden
2
Department of Computer Science and Technology, China University of Mining and Technology, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Buildings 2021, 11(1), 30; https://doi.org/10.3390/buildings11010030
Received: 29 November 2020 / Revised: 7 January 2021 / Accepted: 14 January 2021 / Published: 16 January 2021
(This article belongs to the Special Issue Net-Zero/Positive Energy Buildings and Districts)
In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping. View Full-Text
Keywords: gaussian mixture model; Expectation-Maximization; urban building energy modeling; data acquisition gaussian mixture model; Expectation-Maximization; urban building energy modeling; data acquisition
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MDPI and ACS Style

Han, M.; Wang, Z.; Zhang, X. An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm. Buildings 2021, 11, 30. https://doi.org/10.3390/buildings11010030

AMA Style

Han M, Wang Z, Zhang X. An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm. Buildings. 2021; 11(1):30. https://doi.org/10.3390/buildings11010030

Chicago/Turabian Style

Han, Mengjie; Wang, Zhenwu; Zhang, Xingxing. 2021. "An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm" Buildings 11, no. 1: 30. https://doi.org/10.3390/buildings11010030

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