Evaluation of “Autotune” calibration against manual calibration of building energy models

Chaudhary, Gaurav; Im, Piljae; Sanyal, Jibonananda; O’Neill, Zheng; Garg, Vishal

doi:10.1016/j.apenergy.2016.08.073

Cited by 66 publications

(22 citation statements)

References 86 publications

(112 reference statements)

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“…Maile et al (2012) use data graphs to explain performance issues by comparing predicted and measured data. They and others (Yang and Becerik-Gerber, 2015;Chaudhary et al, 2016;Kim and Park, 2016;Sun et al, 2016;Kim et al, 2017) use statistical variables proposed by Bou-Saada and Haberl (1995), such as the normalized mean bias error and CV(RMSE) given in Equations (1) and (2).…”

Section: Analysis Of Discrepancymentioning

confidence: 99%

Quantifying the Underlying Causes of a Discrepancy Between Predicted and Measured Energy Use

et al. 2019

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Simulation is commonly utilized as a best practice approach to assess building performance in the building industry. However, the built environment is complex and influenced by a large number of independent and interdependent variables, making it difficult to achieve an accurate representation of real-world building energy in-use. This gives rise to significant discrepancies between simulation results and actual measured energy consumption, termed "the performance gap." The research presented in this paper quantified the impact of underlying causes of this gap, by developing building simulation models of four existing non-domestic buildings, and then calibrating them toward their measured energy use at a high level of data granularity. It was found that discrepancies were primarily related to night-time use and seasonality in universities is not being captured correctly, in addition to equipment and server power density being underestimated (indirectly impacting heating and cooling loads). Less impactful parameters were among others; material properties, system efficiencies, and air infiltration assumptions.

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Section: Analysis Of Discrepancymentioning

confidence: 99%

Quantifying the Underlying Causes of a Discrepancy Between Predicted and Measured Energy Use

et al. 2019

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“…The result of this extra energy consumption is that some models with slightly higher energy consumption have better uncertainty temperature results than the best models selected by the energy of the objective function. From a practical point of view, this means that the best model cannot be chosen directly from the results offered by the algorithm unless an uncertainty temperature analysis is subsequently performed, in the same way as other similar works [26,27,30,31].…”

Section: Summary Of the Zec Methodologymentioning

confidence: 99%

Uncertainy’s Indices Assessment for Calibrated Energy Models

et al. 2019

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Building Energy Models (BEMs) are a key element of the Energy Performance of Buildings Directive (EPBD), and they are at the basis of Energy Performance Certificates (EPCs). The main goal of BEMs is to provide information for building stakeholders; they can be a powerful market tool to increase demand for energy efficiency solutions in buildings without affecting the comfort of users, as well as providing other benefits. The next generation of BEMs should value buildings in a holistic and cost-effective manner across several complementary dimensions: envelope performances, system performances, and controlling the ability of buildings to offer flexible services to the grid by optimizing energy consumption, distributed generation, and storage. SABINA is a European project that aims to look for flexibility to the grid, targeting the most economic source possible: existing thermal inertia in buildings. In doing so, SABINA works with a new generation of BEMs that tend to mimic the thermal behavior of real buildings and therefore requires an accurate methodology to choose the model that complies with the requirements of the system. This paper details our novel extensive research on which statistical indices should be chosen in order to identify the best model offered by the calibration process developed by Fernandez et al. in a previous paper and therefore is a continuation of that work.

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“…Therefore, it is difficult to determine whether one method could prevail over the other. Apart of a case in which the difference of the two approaches has been evidenced [38] the literature studies scarcely assess this issue. For this reason, it seems to the authors that the proposed evaluation, even if related to a specific case study, could be informative to the literature.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Automatic and Manual Calibration of an Office Building Energy Model

Cornaro

Bosco

Lauria³

et al. 2019

Applied Sciences

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Featured Application: This work could contribute to evidence strengths and weaknesses of manual and automatic calibration of building dynamic simulation models leading at improving the quality of building retrofit solutions investigation.Abstract: Energy reduction can benefit from the improvement of energy efficiency in buildings. For this purpose, simulation models can be used both as diagnostic and prognostic tools, reproducing the behaviour of the real building as accurately as possible. High modelling accuracy can be achieved only through calibration. Two approaches can be adopted-manual or automatic. Manual calibration consists of an iterative trial and error procedure that requires high skill and expertise of the modeler. Automatic calibration relies on mathematical and statistical methods that mostly use optimization algorithms to minimize the difference between measured and simulated data. This paper aims to compare a manual calibration procedure with an automatic calibration method developed by the authors, coupling dynamic simulation, sensitivity analysis and automatic optimization using IDA ICE, Matlab and GenOpt respectively. Differences, advantages and disadvantages are evidenced applying both methods to a dynamic simulation model of a real office building in Rome, Italy. Although both methods require high expertise from operators and showed good results in terms of accuracy, automatic calibration presents better performance and consistently helps with speeding up the procedure. consumption of a building and consists of the construction of a virtual model of the building that has to reproduce the behaviour of the real building as accurately as possible. The reduction of the energy consumption can be pursued if the energy distribution throughout the building is known and understood. Building modelling can then be used both as a diagnostic tool, to better understand the physics laws of the building, and as a prognostic tool to predict the building's behaviour once the laws are known. Building energy performance simulation (BEPS) can be achieved using data driven (black box) or law driven (white box) models. Considering the white box approach, many BEPS tools are available for example, TRNSYS, Energy Plus, ESPr, and for the most popular, accurate capabilities evaluations and comparisons have been made [10]. Retrofit projects and operations management for buildings require accurate modelling that can be achieved through calibration. Calibration consists of an iterative process where model input is changed to reconcile the model output with the measured data. Although this process can be considered essential for having reliable results, still few practitioners are capable to perform it. Typical variables that are involved in the calibration process are mainly energy consumption [11][12][13][14] and indoor air temperature [11,15,16], for which simulated values are compared to measured ones on a specific time scale. Usually calibration at a small time scale (hourly of sub-hourly) results more difficult and...

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Evaluation of “Autotune” calibration against manual calibration of building energy models

Cited by 66 publications

References 86 publications

Quantifying the Underlying Causes of a Discrepancy Between Predicted and Measured Energy Use

Quantifying the Underlying Causes of a Discrepancy Between Predicted and Measured Energy Use

Uncertainy’s Indices Assessment for Calibrated Energy Models

Effectiveness of Automatic and Manual Calibration of an Office Building Energy Model

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