Building energy model reduction using model-cluster-reduce pipeline

Shi, Zixiao; O’Brien, William

doi:10.1080/19401493.2017.1410572

Cited by 14 publications

(5 citation statements)

References 38 publications

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“…In this application the scale factor is calculated from floor area: Where is the scale factor, is the zone floor area. More details about the model reduction process can be referred to the original paper by the authors (Shi & O'Brien, 2017).…”

Section: Methodsmentioning

confidence: 99%

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Achieving Faster Building Energy Model Optimization through Selective Zone Elimination

Shi¹,

Bucking²,

O’Brien³

2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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Optimization in building performance simulation (BPS) has become increasingly important due to the growing need for high-performance building design and operation. Numerous research efforts have been dedicated to decreasing optimization runtime by introducing improved optimization algorithms and advanced sampling techniques. This paper presents a novel model order reduction (MOR) algorithm tailored for speeding up building energy simulation. The algorithm identifies archetype zones simplifying the needless repetition of thermal zones. For an entire optimization process, this MOR method can be repeated recursively to reproduce reduced models. The proposed method can be used to speed up large-scale simulations including optimization, uncertainty analysis and model predictive controls. Preliminary results with parametric simulations show a runtime reduction of about 76% reduction for 15 simulations while still maintaining the predicted total annual energy consumption within a 10% margin. Further research will be conducted to compare the optimization results when applying the proposed MOR algorithm and determine if the reduced model produces the same optimal design. The proposed method may significantly improve the optimization runtime with a minor effect on optimization accuracy, thus increasing the overall usability of BPS optimizations.

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Section: Methodsmentioning

confidence: 99%

“…This paper focuses on the building energy simulation (BES) aspect of the BPS. It is an extension of a previously published BPS MOR method called Model-Cluster-Reduce (Shi & O'Brien, 2017). The goal is to achieve faster BES optimization by integrating the MOR method with conventional iterative BES optimization.…”

Section: Introductionmentioning

confidence: 99%

Achieving Faster Building Energy Model Optimization through Selective Zone Elimination

Shi¹,

Bucking²,

O’Brien³

2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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“…Other Journal of Building Performance Simulation can be referenced [178]. A preliminary study was also published at the 3 rd Asia International Building Performance Simulation Association…”

Section: Group 1 Groupmentioning

confidence: 99%

“…To reduce building performance simulation time and make fault evaluation more responsive to the diagnostics, a novel model reduction method based on selective zone elimination has also been developed. This method has been formulated and generalized in a publication in Journal of Building Performance Simulation [178]. For large-scale simulations, especially BPS models directly translated from BIM models, this model reduction process could potentially decrease the overall simulation time by 70%-80%, significantly cutting the computation time while keeping the simulation results reasonably reliable.…”

mentioning

confidence: 99%

“…13 Schematics of the Model-Cluster-Reduce Pipeline[178] The time series of hourly heating/cooling load of each thermal zone is used in the model step as an abstraction of the thermal characteristics. The heating/cooling load is a superposition of all the heat transfer components occurring inside each thermal zone, which in theory, may be able to provide enough information to represent the uniqueness or affinities of this zone.…”

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confidence: 99%

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A Probabilistic Distributed Fault Detection, Diagnostics and Evaluation Framework for Building Systems

Shi¹

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The scope of this thesis is to develop an automated fault detection, diagnostic, and evaluation (AFDDE) framework for building systems. This framework aims to provide a holistic approach to detect, identify and evaluate building faults to the stakeholders to facilitate decision-making. It is adaptable to different building systems as well as flexible to both distributed and centralised implementations. The first component of the framework, fault detection, uses a novel technique called constrained dual Extended Kalman Filter (EKF) to estimate system parameters and then generates symptom descriptions described by probability and severity. The fault diagnostic process uses Dynamic Bayesian Network (DBN) with leaky Noisy-Max model to accommodate probabilistic descriptions of faults and symptoms. The fault evaluation aspect of the system employs existing building performance simulation (BPS) tools to estimate quantitative impacts of the diagnosed faults. A model reduction process called "model-cluster-reduce" is also developed to speed up simulation. Each component of the framework is created with the intention to be generalized to other related areas of research such as model predictive control and BPS optimization. Four case studies of both zone-level and air handling unit (AHU)-level are adopted to demonstrate the functionalities of the proposed AFDDE framework. Overall, the framework shows promising results with a short fault diagnosis time, and low false positive and false negative rates, albeit with the tendency of overestimating fault impacts.In addition to the future work to further expand the AFDDE framework, many fundamental research questions also arise from this thesis.iii Acknowledgements First and foremost, I would like to thank my Ph.D. supervisor, Dr. Liam O'Brien for his guidance and supervision throughout my study. It was a great pleasure to work with him, and I would not have accomplished this work without his mentoring and encouragement.He has aspired me to continue my academic career and pursue my research in building science.

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Order Reduction in Linear Dynamical Systems by Using Improved Balanced Realization Technique

Prajapati¹,

Prasad²

2019

Circuits Syst Signal Process

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Building energy model reduction using model-cluster-reduce pipeline

Cited by 14 publications

References 38 publications

Achieving Faster Building Energy Model Optimization through Selective Zone Elimination

Achieving Faster Building Energy Model Optimization through Selective Zone Elimination

A Probabilistic Distributed Fault Detection, Diagnostics and Evaluation Framework for Building Systems

Order Reduction in Linear Dynamical Systems by Using Improved Balanced Realization Technique

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