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

Raillon, L.; Ghiaus, Christian

doi:10.1155/2017/5636145

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…To model the S. J. Carew building, the zonal inputs and outputs must be included in the identification data. There are three main steps in system identification [24][25][26][27]:…”

Section: System Identificationmentioning

confidence: 99%

Energy Consumption Analysis of a Large Building at Memorial University

2019

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In this paper, energy consumption analysis and a process to identify appropriate models based on heat dynamics for large structures are presented. The analysis uses data from heating, ventilation, and air-conditioning (HVAC) system sensors, as well as data from the indoor climate and energy software (IDA Indoor Climate and Energy (IDA-ICE) 4.7 simulation program). Energy consumption data (e.g., power and hot water usage) agrees well with the new models. The model is applicable in a variety of applications, such as forecasting energy consumption and controlling indoor climate. In the study, both data-derived models and a grey-box model are tested, producing a complex building model with high accuracy. Also, a case study of the S. J. Carew building at Memorial University, St. John’s, Newfoundland, is presented.

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“…To model the S. J. Carew building, the zonal inputs and outputs must be included in the identification data. There are three main steps in system identification [24][25][26][27]:…”

Section: System Identificationmentioning

confidence: 99%

Energy Consumption Analysis of a Large Building at Memorial University

2019

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“…"Grey-box" models can be also converted to "black-box" (i.e., statistical and machine learning models) for specific applications, for example control [204] or monitoring of internal conditions [205,206]. "Black box" models are computationally efficient but they need to be trained on data before being deployed.…”

Section: Harmonizing Methodologies To Analyse Energy Performancementioning

confidence: 99%

Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector

2021

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Decarbonisation and efficiency goals set as a response to global warming issue require appropriate decision-making strategies to promote an effective and timely change in energy systems. Conceptualization of change is a relevant part of energy transitions research today, which aims at enabling radical shifts compatible with societal functions and market mechanisms. In this framework, construction sector can play a relevant role because of its energy and environmental impact. There is, however, the need to move from general instances to specific actions. Open data and open science, digitalization and building data interoperability, together with innovative business models could represent enabling factors to accelerate the process of change. For this reason, built environment research has to address the co-evolution of technologies and human behaviour and the analytical methods used for this purpose should be empirically grounded, transparent, scalable and consistent across different temporal/spatial scales of analysis. These features could potentially enable the emergence of “ecosystems” of applications that, in turn, could translate into projects, products and services for energy transitions in the built environment, proposing innovative business models that can stimulate market competitiveness. For these reasons, in this paper we organize our analysis according to three levels, from general concepts to specific issues. In the first level, we consider the role of building energy modelling at multiple scales. In the second level, we focus on harmonization of methods for energy performance analysis. Finally, in the third level, we consider emerging concepts such as energy flexibility and occupant-centric energy modelling, considering their relation to monitoring systems and automation. The goal of this research is to evaluate the current state of the art and identify key concepts that can encourage further research, addressing both human and technological factors that influence energy performance of buildings.

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“…The approach proposed at the normative level for building performance simulation (ISO 52016-1) is substantially similar, in principles, to research focused on lumped parameter modelling using resistancecapacitance (RC) analogy [29] and analytical calculations [30,31]. The conversion of RC models in state-space form and then in time series models is described in detail in recent literature [32,33]. More in general, the use of reduced order models for building performance simulation is an active research field at present, with multiple possible applications [34].…”

Section: Methodsmentioning

confidence: 99%

From in-situ measurement to regression and time series models: An overview of trends and prospects for building performance modelling

Manfren

Nastasi

2019

AIP Conference Proceedings

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Data analysis methodologies are crucial to learn insights from data and to create more trust in the assumptions used for energy performance assessment. Indeed, continuous performance monitoring should become a more diffuse practice in order to improve our design and operation strategies for the future. This is an essential step to reduce incrementally the gap between simulated and measured performance. In fact, assumptions in simulation represent a significant source of uncertainty when estimating the energy performance of buildings. This uncertainty affects decisionmaking processes in multiple ways, from design of new and refurbished buildings to policy making. The research presented aims to highlight potential links between experimental approaches for test-facilities and methods and tools used for continuous performance monitoring, at the state of the art. In particular, we start by exploring the relation between in-situ measurement of thermal transmittance (U) and regression-based monitoring approaches, such as co-heating test and energy signature, for heat load coefficient (HLC) and solar aperture (gA) estimation. After that, we highlight some recent developments in simplified dynamic energy modelling using lumped parameter models. In particular, we want to underline the scalability of these techniques, considering relevant issues in current integrated engineer design perspective. These issues include, among others, the necessity of limiting the number of a sensors to be installed in buildings, the possibility of employing both experimental and real operation data (and compare them with design data as well) and, finally, the possibility to automate performance monitoring at multiple scales, from single components, to individual buildings, to building stock and cities.

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

Cited by 4 publications

References 12 publications

Energy Consumption Analysis of a Large Building at Memorial University

Energy Consumption Analysis of a Large Building at Memorial University

Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector

From in-situ measurement to regression and time series models: An overview of trends and prospects for building performance modelling

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