Development and validation of grey-box models for forecasting the thermal response of occupied buildings

Harb, Hassan; Boyanov, Neven; Hernández, Leonardo; Streblow, Rita; Müller, Dirk

doi:10.1016/j.enbuild.2016.02.021

Cited by 141 publications

(86 citation statements)

References 24 publications

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“…Berthou et al [11] and Bacher and Madsen [12] performed comparative studies of various thermal network model structures. Similar studies to find proper model orders for building systems can also be found in [13,14,15,16]. Madsen and Holst [17] applied the maximum likelyhood estimation algorithm (MLE) to tune model parameters.…”

Section: Introductionmentioning

confidence: 91%

System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach

Kim

Cai

Ariyur

et al. 2016

Building and Environment

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Identification approaches applied to semi-physical thermal network structures, so called gray-box modeling approaches, are popular in building science for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. However conventional identification approaches applied to thermal networks fail when there are significant unmeasured heat gains that influence building responses. This paper presents a method to obtain improved gray-box building models from closed loop data having significant unmeasured disturbances. The method estimates both physical parameters of a building thermal network model and also a disturbance model that characterizes the unmeasured inputs. The performance of the algorithm is demonstrated using numerical and experimental results.

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Section: Introductionmentioning

confidence: 91%

System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach

Kim

Cai

Ariyur

et al. 2016

Building and Environment

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“…The grey-box models provide some advantages in the buildings' thermal modeling process, in particular, ease of their use and the possibility to link their parameters to global buildings' physical characteristics, such as the heat resistance and the mass capacity. These models can be used for different purposes such as control of the indoor environment [8,9], forecasting energy consumption, and evaluating buildings' energy performance [10][11][12]. However, their practical use is subjected to the difficulty of the determination of their optimal order.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

et al. 2019

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The use of grey-box models for short-time forecasting of buildings' thermal behavior requires the determination of the models' order since this order could influence the grey-box models' performance. This paper presents an analysis of the optimal order of these models for different thermal conditions. The novelty of this work consists of considering the influence of the heating conditions on the determination of the performances of grey-box models. The analysis is based on experimental tests that were conducted in a room with different thermal conditions, related to the variation of the heating power. Experimental results were used for the determination of the optimal grey-box models' order that minimizes the gap between the experimental results and the grey-box forecasting. Results show that the optimal grey-box models' order depends on the buildings' thermal conditions, but generally lies between two and three with an error less than 0.2 • C and a fit percent greater than 90%.

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“…Model based control techniques have been developed to ensure building equipment functions in an energy-efficient manner [2]. However, these control techniques need detailed building models, which require extensive monitoring and are not often the cost-effective solution [3]. This can be attributed to the fact that the nature of building operation is usually uncertain and depends on various factors, such as, usage and physical properties.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these models are accurate and computationally efficient. The design approach of grey-box models is often application specific, for instance, the design approach for grey box modelling of commercial buildings differs on a case by case basis [3]. Furthermore, the scalability of these models is limited by the network order, which defines the level of complexity incorporated in the model.…”

Section: Introductionmentioning

confidence: 99%

A generalization approach for reduced order modelling of commercial buildings

Shamsi

Ali

O’Donnell

2019

Journal of Building Performance Simulation

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Grey-box techniques can counter the computational inefficiency and resource-intensive nature of the conventional complex whitebox models. However, these approaches might tend to be too specific in their application and scalability is limited by network order. To overcome these challenges, this study proposes a generalized approach for selection of reduced-order RC network models for commercial buildings using the peak power consumption characterization. The devised methodology is used to design the RC networks of buildings connected to district heating network at University College Dublin. The close proximity between measured and simulated demand indicate the influence of power demand on RC network selection.

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Development and validation of grey-box models for forecasting the thermal response of occupied buildings

Cited by 141 publications

References 24 publications

System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach

System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach

Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

A generalization approach for reduced order modelling of commercial buildings

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