Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches

Donovan, Adam; Sullivan, Paul D.; Murphy, Michael D.

doi:10.1016/j.apenergy.2019.04.082

Cited by 54 publications

(36 citation statements)

References 52 publications

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“…The NBERT has one large open plan office area, two small cellular offices, one conference room and one seminar room, with a corridor connecting all occupied zones. For information relating to the thermo-physical properties and the building geometry see [56,61,62], for information regarding the natural ventilation system see [54] and for specification of the instrumentation used in recording the empirical data see [23]. The data used in this paper as well as building information regarding the NBERT test-bed can be found at messo.cit.ie/nbert.…”

Section: Applicationmentioning

confidence: 99%

“…However, WB models were found to be inaccurate when compared to measured data from real buildings [21]. Applying manual or automated calibration techniques to WB models has been shown to greatly improve accuracy for internal air temperature prediction in real buildings [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Reaching nZEB standards requires improvements in fabric and energy performance when compared to existing buildings. Although energy performance certificates suggest that many nZEBs exist [46], the sample of well documented case study examples in published literature is limited [23,[47][48][49][50]. Over 80% of the simulation studies that focused on modelling and performance of actual nZEBs, identified occupant behaviour (OB) as the primary inhibiting factor for accurate simulation and predictions of both energy and internal air temperatures [23,49,[51][52][53].…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the near stochastic nature of OB in certain conditions. OB has a strong influence on the internal air temperatures in nZEBs [23], as these buildings are highly insulated and can be thermally decoupled from external climatic conditions. As a result, the internal thermal gains from OB have a more dominant effect on internal air temperature.…”

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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Section: Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…We utilize the tolerance of the CV (RMSE) in the energy use issue in this study. According to the ASHRAE Guideline 14, a CV (RMSE) lower than 20% indicates a good model fit with acceptable predictive capabilities [81]. That the CV (RMSE) of GPR is 0.009% means that this model explained the relationship between new business vitality and business environment well.…”

Section: Feature Importancementioning

confidence: 99%

Relationship between Urban New Business Indexes and the Business Environment of Chinese Cities: A Study Based on Entropy-TOPSIS and a Gaussian Process Regression Model

Shi

Liu

2020

Sustainability

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The interactive development of economic globalization, informatization, marketization, and urbanization has reshaped the urban commercial landscape and society, and poses new requirements for the business environment. New commerce forms that are based on information technology and electronic payment and integrate online and offline forms are growing rapidly in China. However, the relationship between new commerce forms and the business environment has not received sufficient academic attention. Using 29 major cities in China, this paper constructs a new business index system consisting of the following six sub-indexes: the characteristic hotels index, the Starbucks index, the Freshhema index, the concept bookstores index, the smart convenience stores index, and the healthcare and medical examination index. The entropy coupled with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method was used for quantitative evaluation of urban new business vitality. We found that the Freshhema index and smart convenient store index are the two most important evaluation factors. The relationship between the new business index and the business environment was examined through multiple linear regression (MLR) and Gaussian process regression (GPR) analysis. We found that the MLR is not a valid model, and instead, the nonlinear GPR model has good explanatory power for this relationship. The results show that human capital has a more important effect than the economic development level on business vitality. The rise and development of new commercial forms depend on the innovation and optimization of the business environment.

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Technical Overview of ZEB for Real‐World Problems

Jegan,

Nagathevan,

Shruti

et al. 2024

Smart Grids as Cyber Physical Systems

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Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches

Cited by 54 publications

References 52 publications

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

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

Relationship between Urban New Business Indexes and the Business Environment of Chinese Cities: A Study Based on Entropy-TOPSIS and a Gaussian Process Regression Model

Technical Overview of ZEB for Real‐World Problems

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