Data-driven modeling of building thermal dynamics: Methodology and state of the art

Wang, Zequn; Chen, Yuxiang

doi:10.1016/j.enbuild.2019.109405

Cited by 69 publications

(20 citation statements)

References 96 publications

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“…This research partially agrees with this study: the ET method was among the best performing methods, but the ANN model outperformed the ET method. Wang and Chen [78] compared three data-driven models, a linear black-box model (ARX), a non-linear black-box model (ANN), and a gray box model in predicting the indoor temperature of a single-zone house. The performance of the gray box model was intermediate between the other two models.…”

Section: Resultsmentioning

confidence: 99%

Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

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Section: Resultsmentioning

confidence: 99%

Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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“…A strong research effort has been made in the last years to model buildings' thermal energy consumption. In particular, models have been developed, [14,15], to describe the thermal dynamics in buildings, whether residential or commercial. Those models can be grouped into three macro categories: (i) White-box, (ii) Black-box, and (iii) Grey-box.…”

Section: Related Workmentioning

confidence: 99%

An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

et al. 2020

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Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.

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“…RC thermal networks are among the most popular grey-box models, which many authors have used to represent building thermal dynamics. Wang and Chen [13] select a 3R2C network as the most appropriate of several RC networks to represent a house with a large glazing area. While this network had the second-best fit (72.60% vs. 73.59%), it is selected because the parameters retain physical meaning.…”

Section: Introductionmentioning

confidence: 99%

“…Particular cases of the PEM arise depending of the objective function used. For example, the least-squares estimator defines the quadratic norm, while the maximum likelihood estimator, defines a likelihood function [13].…”

Section: Introductionmentioning

confidence: 99%

Inspection of the Identification of Thermal Modeling Process in Buildings: A Proposed Methodology for a Case Study in Tropical Climate

Rivera

Sánchez

Mora

et al. 2021

Advances in Automation and Robotics Research

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A methodology to identify thermal models based on RC networks is proposed and verified in two case studies located in Panama City. Data for identification is obtained through simulation via a whitebox model for a whole year. The dispersion of the parameter estimation is studied by training the models with different datasets. The models based on the 1R1C and 2R1C networks are the only ones with consistent estimates, hence, identifiable parameters. The 2R1C model that incorporates the mean radiant temperature as an input is notably better at describing the thermal dynamics, obtaining an RMSE of 0.5386 • C during validation for a training set of six months.

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Data-driven modeling of building thermal dynamics: Methodology and state of the art

Cited by 69 publications

References 96 publications

Use of Machine Learning Methods for Indoor Temperature Forecasting

Use of Machine Learning Methods for Indoor Temperature Forecasting

An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

Inspection of the Identification of Thermal Modeling Process in Buildings: A Proposed Methodology for a Case Study in Tropical Climate

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