A new analytical approach for simplified thermal modelling of buildings: Self-Adjusting RC-network model

Jara, Enrique Ángel Rodríguez; Flor, Francisco José Sánchez de la; Domínguez, Servando Álvarez; Félix, José Luis Molina; Lissén, José Manuel Salmerón

doi:10.1016/j.enbuild.2016.08.039

Cited by 36 publications

(10 citation statements)

References 12 publications

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“…R-C (resistance-capacitance) models are often used for their simplicity, even if they only give a rough estimate of the energy use. Reduced-order grey box models can behave better, but the estimation of parameters should be continuously updated [9,11]. For example Heidarinejad et al [12] developed effective urban-scale reduced-order building energy models by checking the most influent variables for the heat transfer process.…”

Section: Scalabilitymentioning

confidence: 99%

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

Arteconi

2018

Environments

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The urgent need for the reduction of greenhouse gas (GHG) emissions requires efficient and integrated energy communities in order to decrease final energy demand and sustain energy transitions towards renewable energy sources (RES). This introduces a big research challenge for future energy systems design and optimization, given the multi-level and inter-sectorial dimensions of the problem. Buildings cover a central role in this context, because they represent the nexus between different energy carrier networks. The study of the management of multi-sector and multi-energy systems presents several challenges, such as the need to take into account uncertainties, to represent the heterogeneity of the energy demand and the scalability of the problem. This paper aims at outlining such a framework, and at showing the state-of-the-art nature of the above-mentioned challenges.

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Section: Scalabilitymentioning

confidence: 99%

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

Arteconi

2018

Environments

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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%

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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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“…Simplified models with a dominant mathematical component can be classified into three groups: The search for the optimal refurbishment project based on optimization [53,60], those using a strong mathematical basis to replace the physical component in the estimation of the energy demand of buildings [65,66], and finally, those that increase the scale of the study to districts or even full cities [67][68][69]. This mathematical methodology requires a large amount of accurately measured consumption data since the models are tailor-made depending on the data.…”

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confidence: 99%

Systematic Simplified Simulation Methodology for Deep Energy Retrofitting Towards Nze Targets Using Life Cycle Energy Assessment

et al. 2019

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The reduction of energy consumption in the residential sector presents substantial potential through the implementation of energy efficiency improvement measures. Current trends involve the use of simulation tools which obtain the buildings’ energy performance to support the development of possible solutions to help reduce energy consumption. However, simulation tools demand considerable amounts of data regarding the buildings’ geometry, construction, and frequency of use. Additionally, the measured values tend to be different from the estimated values obtained with the use of energy simulation programs, an issue known as the ‘performance gap’. The proposed methodology provides a solution for both of the aforementioned problems, since the amount of data needed is considerably reduced and the results are calibrated using measured values. This new approach allows to find an optimal retrofitting project by life cycle energy assessment, in terms of cost and energy savings, for individual buildings as well as several blocks of buildings. Furthermore, the potential for implementation of the methodology is proven by obtaining a comprehensive energy rehabilitation plan for a residential building. The developed methodology provides highly accurate estimates of energy savings, directly linked to the buildings’ real energy needs, reducing the difference between the consumption measured and the predictions.

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A new analytical approach for simplified thermal modelling of buildings: Self-Adjusting RC-network model

Cited by 36 publications

References 12 publications

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

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

Systematic Simplified Simulation Methodology for Deep Energy Retrofitting Towards Nze Targets Using Life Cycle Energy Assessment

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