The development and validation of the linear time varying Simulink-based model for the dynamic simulation of the thermal performance of buildings

Michalak, Piotr

doi:10.1016/j.enbuild.2017.02.047

Cited by 25 publications

(17 citation statements)

References 19 publications

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“…In the original design of the building, Passive House Planning package (PHPP) [52] was used for simulation. Instead, in this study we used a validated grey-box dynamic model [53,54] to perform multiple simulation runs in a reduced time frame. Indeed, grey-box models are very flexible and can be used in the inverse mode to estimate lumped properties of the actual building, eventually extending their applicability with Bayesian analysis [55,56] or Dempster-Shafer theory of the evidence [57].…”

Section: Methodsmentioning

confidence: 99%

Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings

Manfren

Nastasi

2020

Energies

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High efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of simulation results remains an issue. Further, empirical evidences indicate that the gap between predicted and measured performance can be quite large owing to different types of errors made in the building life cycle phases. Consequently, the discrepancy between a priori performance assessment and a posteriori measured performance can hinder the development and diffusion of energy efficiency practices, especially considering the investment risk. The approach proposed in the research is rooted on the integration of parametric simulation techniques, adopted in the design phase, and inverse modelling techniques applied in Measurement and Verification (M&V) practice, i.e., model calibration, in the operation phase. The research focuses on the analysis of these technical aspects for a Passive House case study, showing an efficient and transparent way to link design and operation performance analysis, reducing effort in modelling and monitoring. The approach can be used to detect and highlight the impact of critical assumptions in the design phase as well as to guarantee the robustness of energy performance management in the operational phase, providing parametric performance boundaries to ease monitoring process and identification of insights in a simple, robust and scalable way.Energies 2020, 13, 621 2 of 14 by assessing transparently the impact of human and technical factors [11]. With respect to human factors in particular, the effects of occupants' behaviour [12] and of their comfort preferences [13] on building performance are generally overlooked in the design phase. This paper aims to present a way to integrate modelling methodologies used across building life cycle phases, from design to operation, in a simple and scalable way. A residential building has been chosen as a case study. The building is a detached single family certified Passive House built in Italy, in the Province of Forlì-Cesena, in the Emilia Romagna region. It has been monitored for three years, learning incrementally insights by comparing the original design phase simulation data with actual measured data. Background and MotivationThe research work answers to the necessity of linking parametric performance analysis and model calibration from a conceptual and practical point of view. Building performance parametric and probabilistic analysis is an essential tool today to ensure robustness of performance and the importance of the Design of Experiments (DOE) is becoming clear [14-17], both for new and retrofitted buildings [18,19]. For example, accounting for the robustness of performance estimates with respect to economic indicators (e.g., in cost-optimal analysis [20-22]) is important because uncertainty can aff...

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Section: Methodsmentioning

confidence: 99%

Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings

Manfren

Nastasi

2020

Energies

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“…Therefore, they are less computationally expensive and require fewer building descriptive parameters [3,4]. The most commonly utilised GB models in thermal engineering applications are resistive-capacitive (RC) models [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] (Table 1). An RC model uses an electrical analogy to describe model structure and function, where resistors (R) and capacitors (C) are used to simulate the thermal energy flow and storage in the building.…”

Section: Introductionmentioning

confidence: 99%

“…For GB models, several studies use manual [5,11,14] and semi-automated calibration approaches [12]. However, the most common approach is automated calibration [3,4,13,[15][16][17]. The majority of studies using RC models for internal air temperature prediction have either relied on synthetic data generated by WB models or a higher order GB model for calibration and validation [3,[6][7][8]11,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…However, the most common approach is automated calibration [3,4,13,[15][16][17]. The majority of studies using RC models for internal air temperature prediction have either relied on synthetic data generated by WB models or a higher order GB model for calibration and validation [3,[6][7][8]11,[15][16][17]. When empirical data are employed for such purposes, it is most commonly recorded data from unoccupied test-cells or rooms [5,12,14].…”

Section: Introductionmentioning

confidence: 99%

“…The observed efficacy of parameter tuning for RC models may be lower if measured empirical data from nZEBs are used (as opposed to synthetic data) due to the included noise from OB. The majority of published research has focused on RC model calibration of air-conditioned buildings [3,[6][7][8]13,16,17]. As these buildings incorporated controlled air-conditioning systems for regulating the internal environment, the influence of OB on air temperature was attenuated, therefore, the model calibration and validation processes are less susceptible to the negative effects of OB noise.…”

Section: Introductionmentioning

confidence: 99%

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Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods

et al. 2021

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In this study, a grey box (GB) model for simulating internal air temperatures in a naturally ventilated nearly zero energy building (nZEB) was developed and calibrated, using multiple data configurations for model parameter selection and an automatic calibration algorithm. The GB model was compared to a white box (WB) model for the same application using identical calibration and validation datasets. Calibrating the GB model using only one week of data produced very accurate results for the calibration periods but led to inconsistent and typically inaccurate results for the validation periods (root mean squared error (RMSE) in validation periods was 229% larger than the RMSE in calibration periods). Using three weeks of data from varying seasons for calibration reduced the model accuracy in the calibration period but substantially increased the model accuracy and generalisation abilities for the validation period, reducing the mean RMSE by over 160%. The use of one week of data increased the standard deviation in parameter selections by over 40% when compared with the three-week calibration datasets. Utilising data from multiple seasons for calibration purposes was found to substantially improve generalisation abilities. When compared to the WB model, the GB model produced slightly less accurate results (mean RMSE of the GB model was 1.5% higher). However, the authors found that employing GB modelling with an automatic model calibration technique reduced the human labour input for simulating internal air temperature of a naturally ventilated nZEB by approximately 90%, relative to WB modelling using a manually calibrated approach.

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Data-driven two-step identification of building thermal characteristics: A case study of office building

et al. 2022

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The development and validation of the linear time varying Simulink-based model for the dynamic simulation of the thermal performance of buildings

Cited by 25 publications

References 19 publications

Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings

Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings

Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods

Data-driven two-step identification of building thermal characteristics: A case study of office building

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