Agent-based system identification for control-oriented building models

Joe, Jaewan; Karava, Panagiota

doi:10.1080/19401493.2016.1212272

Cited by 14 publications

(11 citation statements)

References 54 publications

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“…A control-oriented building model was estimated with actual building operation data and then input to a state-space formulation for optimization in MPC. The grey-box model structure was applied, which is simple and robust so has been applied for a number of MPC studies [29,30]. The experiment was conducted for four days (21-24 September 2019) to generate building data from the test-bed.…”

Section: Case 2: Model-based Predictive Control (Mpc)mentioning

confidence: 99%

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Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Joe

Dong

2020

Sustainability

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This study provides a general procedure to generate a direct expansion (DX) cooling coil system for a roof top unit (RTU), which is a typical heating ventilation and air-conditioning (HVAC) system for commercial buildings in the United States. Experimental data from a full-scale unoccupied 2-story commercial building is used for the HVAC modeling. The regression for identifying the model coefficients was carried out with multiple stages, and the results were validated with measured data. The model’s applicability was evaluated with multiple case studies, including a building energy simulation (BES) program validation, model-based predictive control (MPC), and fault diagnostics and detection (FDD).

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Section: Case 2: Model-based Predictive Control (Mpc)mentioning

confidence: 99%

“…number of MPC studies [29,30]. The experiment was conducted for four days (21-24 September 2019) to generate building data from the test-bed.…”

Section: Case 2: Model-based Predictive Control (Mpc)mentioning

confidence: 99%

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Joe

Dong

2020

Sustainability

Self Cite

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“…Some criteria are similar to the sum of squared residuals defined in Eq. 12, and are sometimes used for an easier interpretation of model fitness: the root mean square error [Joe and Karava, 2017], mean absolute percentage error [Dong and Lam, 2014], etc.…”

Section: Least Square Estimationmentioning

confidence: 99%

“…• [Joe and Karava, 2017] draw ACF and CCF profiles after estimation in a deterministic agent-based framework and found relatively high values for these functions.…”

Section: Residual Analysismentioning

confidence: 99%

Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data

Rouchier

2018

Energy and Buildings

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Building physics researchers have benefitted from elements of statistical learning and time series analysis to improve their ability to construct knowledge from data. What is referred to here as inverse problems are actually a very broad field that encompasses any study where data is gathered and mined for information. The purpose of the present article is twofold. First, it is a tutorial on the formalism of inverse problems in building physics and the most common ways to solve them. Then, it provides an overview of tools and methods that can either be used to assess or the reliability of inverse problem results, prevent erroneous interpretation of data, and optimise information gained by experiments. It provides an introduction, along with useful references, to the topics of estimation error assessment, regularisation, identifiability analysis, residual analysis, model selection and optimal experiment design. These concepts are presented in the context of building simulation and energy performance assessment: a simple RC model is used as a running example to illustrate each chapter.

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“…Generally, grey-box models have the advantage of keeping a physical-based structure while reducing the level of information details about the building, since only rough initial guesses are needed as starting point for the calibration procedure [24]. Moreover, grey-box models typically require shorter periods of data than black-box models for their calibration [25]. This semi-physical interpretation of grey-box models, together with the reduced dataset required, makes them suitable for model order reduction procedures aimed at adjusting their complexity to the specific application required, such performance assessments and smart controls [26].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation

et al. 2019

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The present paper introduces an iterative methodology to progressively reduce building simulation model complexity with the aim of identifying potential trade-offs between computational requirements (i.e., model complexity) and energy estimation accuracy. Different levels of model complexity are analysed, from commercial building energy simulation tools to low order calibrated thermal networks models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model. The validation process was performed in terms of internal temperature profiles and building thermal energy demand predictions. Synthetic profiles were generated from the validated models and used for calibrating high order models. A reduction (trimming) procedure was applied to reduce the model complexity using an energy performance criterion prior to model trimming. The proposed methodology has the advantage of keeping the physical structure of the original RC model, thus enabling the use of the trimmed lumped parameter building model for other applications. The analysis showed that it is possible to reduce the model complexity by half, while keeping the accuracy above 90% for the targeted building.

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Agent-based system identification for control-oriented building models

Cited by 14 publications

References 54 publications

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data

An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation

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