International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings, Volume I

Committee, IPMVP

doi:10.2172/776003

Cited by 42 publications

(34 citation statements)

References 3 publications

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“…As ASHRAE Guideline 14-2002 and 14-2014 explain, the uncertainty analysis is the "(b) process of determining the degree of confidence in the true value when using measurement procedures and/or calculations" [8,9]. The three main sources that explain how to determine this "degree" of confidence, its uncertainty, are ASHRAE Guideline 14, FEMP [10][11][12] and IPVMP [13][14][15][16]. They use simplified methods to quantify the uncertainty in saving computations and, as ASHRAE Guideline 14 explains, others can be used if they are compliant with the Guideline (Subsection 5.2.11 [9] or 4.2.11 [8]).…”

Section: Measuring Uncertaintymentioning

confidence: 99%

Validation of Calibrated Energy Models: Common Errors

2017

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Nowadays, there is growing interest in all the smart technologies that provide us with information and knowledge about the human environment. In the energy field, thanks to the amount of data received from smart meters and devices and the progress made in both energy software and computers, the quality of energy models is gradually improving and, hence, also the suitability of Energy Conservation Measures (ECMs). For this reason, the measurement of the accuracy of building energy models is an important task, because once the model is validated through a calibration procedure, it can be used, for example, to apply and study different strategies to reduce its energy consumption in maintaining human comfort. There are several agencies that have developed guidelines and methodologies to establish a measure of the accuracy of these models, and the most widely recognized are: ASHRAE Guideline 14-2014, the International Performance Measurement and Verification Protocol (IPMVP) and the Federal Energy Management Program (FEMP). This article intends to shed light on these validation measurements (uncertainty indices) by focusing on the typical mistakes made, as these errors could produce a false belief that the models used are calibrated.

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Section: Measuring Uncertaintymentioning

confidence: 99%

Validation of Calibrated Energy Models: Common Errors

2017

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“…ASHRAE Guidelines 14 [43,44], Federal Energy Management Program (FEMP) 3.0 [45,46], and International Performance Measurements and Verification Protocol (IPMVP) [3] recommend limits in order to consider an energy model as calibrated. Table 2 shows the limits of the different documents.…”

Section: The Design Of the Experimentsmentioning

confidence: 99%

“…The International Performance Measurements and Verification Protocol (IPMVP) [3] classifies the computer simulation (similar to Asset Rating) as option D and the meter approach (similar to Operational Rating) as Option C. However, the two are thought of as "Whole Building" approaches. These approaches have achieved good results when an evaluation of the building energy performance is at stake.…”

Section: Introductionmentioning

confidence: 99%

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Towards a New Generation of Building Envelope Calibration

Bandera

Ruiz

2017

Energies

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Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change 21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simplified past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model.

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“…It follows naturally the reassessment on the relevance of the predicted gains and how the return of investment can be certified. A solution could be to measure the energy consumption in a reference situation, before and after refurbishment and determine the savings [7]. During the baseline period, the model is calibrated on measurements.…”

Section: Introductionmentioning

confidence: 99%

Study of Error Propagation in the Transformations of Dynamic Thermal Models of Buildings

Raillon¹,

Ghiaus²

2017

Journal of Control Science and Engineering

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Dynamic behaviour of a system may be described by models with different forms: thermal (RC) networks, state-space representations, transfer functions, and ARX models. These models, which describe the same process, are used in the design, simulation, optimal predictive control, parameter identification, fault detection and diagnosis, and so on. Since more forms are available, it is interesting to know which one is the most suitable by estimating the sensitivity of the model to transform into a physical model, which is represented by a thermal network. A procedure for the study of error by Monte Carlo simulation and of factor prioritization is exemplified on a simple, but representative, thermal model of a building. The analysis of the propagation of errors and of the influence of the errors on the parameter estimation shows that the transformation from state-space representation to transfer function is more robust than the other way around. Therefore, if only one model is chosen, the state-space representation is preferable.

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International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings, Volume I

Cited by 42 publications

References 3 publications

Validation of Calibrated Energy Models: Common Errors

Validation of Calibrated Energy Models: Common Errors

Towards a New Generation of Building Envelope Calibration

Study of Error Propagation in the Transformations of Dynamic Thermal Models of Buildings

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