A Resistance–Capacitance Model for Real-Time Calculation of Cooling Load in HVAC-R Systems

Fayazbakhsh, M.A.; Bagheri, Farshid; Bahrami, Majid

doi:10.1115/1.4030640

Cited by 21 publications

(9 citation statements)

References 25 publications

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“…A systematic modelling approach outlined in Figure 2, using a methodology based on existing published studies [26][27][28]33] was adopted to capture the complexity of this application. The main aim of this approach is to adequately reproduce, and subsequently predict, the internal air temperature ramps within the indoor occupied space being modelled during shut down of the AHU, based on a number of defined input variables.…”

Section: Description Of the Modelmentioning

confidence: 99%

“…Considerable research has also been conducted to date into modelling the internal temperature conditions of naturally ventilated office spaces [26,27]. A variety of modelling concepts have been employed in this area, with Grey Box (GB) models [26] and Resistance Capacitance (RC) techniques [28] being the predominant methodologies. In modelling terms, White Box (WB) models are physics-based and mechanistic in operation, typically utilising a large number of building descriptive parameters and system specifications, whereas black box models are generally data-driven and use complex algorithms to map the relationship between system inputs and outputs using large amounts of empirical data.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, GB models are less computationally demanding and require fewer building descriptive parameters [26]. RC models are based on the shared relationship between electrical circuits and thermal systems, where the thermal energy flow and storage in a building can be represented by resistors (R) and capacitors (C), similarly to those in an electrical circuit [28]. In the industrial sector, studies have been conducted evaluating the synergies between manufacturing heat sources and HVAC loads to reduce peak power demand [29] as well as exploring passive measures for preventing overheating in an industrial building [30].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

2021

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Increasing participation in demand response within the industrial sector may be crucial to growing the levels of available flexible capacity required to reliably control national electricity grids as renewable generation increases to satisfy emission targets. This research aims to assist the uptake of demand response in the industrial sector by investigating risk to indoor thermal environments on industrial sites offering air handling unit capacity for demand response. This evaluation uses a systematic model-based approach, calibrated and validated with empirical data from a relevant case study industrial building to assess risk through a number of scenarios. The conditions investigated cover several relevant grid response times and durations, and national and international extreme external ambient temperatures in the past, present and future under a variety of temperature limits. The study demonstrated that there is very low risk to the case study site participating in demand response, with only 15 of 264 initial and 284 of 936 total scenarios triggering any risk. The major factors affecting risk levels identified were more stringent temperature limits and the influence of more extreme climates. The development and implementation of this concept has considerable potential to benefit industrial participants and the wider national electricity grids.

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Section: Description Of the Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

2021

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“…For example, 4R1C [13,23], 3R2C [31,41], and 2R1C [12] networks have been used to model the building envelope, while 1C [23], 1R1C [13,41], and 2R2C [31] networks have been used to represent the building spaces. The majority of studies that build an RC model assume the knowledge of the building insulation, thermal mass, floor area, layout, and construction material [4,7,10,38]. Leveraging this knowledge can indeed greatly reduce the complexity of model training.…”

Section: Related Workmentioning

confidence: 99%

Identifying Grey-box Thermal Models with Bayesian Neural Networks

Hossain,

Zhang,

Ardakanian

2020

Preprint

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Smart thermostats are one of the most prevalent home automation products. They learn occupant preferences and schedules, and utilize an accurate thermal model to reduce the energy use of heating and cooling equipment while maintaining the temperature for maximum comfort. Despite the importance of having an accurate thermal model for the operation of smart thermostats, fast and reliable identification of this model is still an open problem. In this paper, we explore various techniques for establishing a suitable thermal model using time series data generated by smart thermostats. We show that Bayesian neural networks can be used to estimate parameters of a grey-box thermal model if sufficient training data is available, and this model outperforms several black-box models in terms of the temperature prediction accuracy. Leveraging real data from 8,884 homes equipped with smart thermostats, we discuss how the prior knowledge about the model parameters can be utilized to quickly build an accurate thermal model for another home with similar floor area and age in the same climate zone. Moreover, we investigate how to adapt the model originally built for the same home in another season using a small amount of data collected in this season. Our results confirm that maintaining only a small number of pre-trained thermal models will suffice to quickly build accurate thermal models for many other homes, and that 1 day smart thermostat data could significantly improve the accuracy of transferred models in another season.

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“…There are several additional advantages to the RC method: "Physical interpretation of the system; ability to directly compare values of parameters of models to known values; possibility to skip model validation; and popularity of this method which means it can be used as common language between different groups and approaches" [9]. Additionally, the RC method is very flexible, accepting various structures and parts of buildings (for example, the influence of the HVAC system can be modeled only as a simple heat source [10]) and enabling different levels of complexity (a whole zone or building can be modeled as a single RC structure [11]).…”

Section: Introductionmentioning

confidence: 99%

Detailed Thermodynamic Modeling of Multi-Zone Buildings with Resistive-Capacitive Method

Belić¹,

Slišković

Hocenski

2021

Energies

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Increased use of energy in buildings and HVAC systems requires advanced control schemes like model-based control to improve energy efficiency, which in turn requires accurate thermodynamic models of buildings. The Resistive-Capacitive (RC) method is a popular and versatile approach for thermal modeling of buildings. Despite this, it is not easy to find practical solutions of implementation of the RC method. It is the goal of this paper to clarify the RC method and demonstrate simple implementation of this method, especially for multi-zone buildings, which have more potential for energy savings from use of model-based control. This paper provides two contributions. First is a detailed explanation of the RC method, focusing on its use for developing a structure of a model and first-principles approach for estimation of parameters of a model. Second is a demonstration of an algorithm that enables automatic development of the structure of a model from basic information about a building (layout, construction elements) and its combination with data-based parameter estimation. Use of the algorithm is presented with a case-study on industrial multi-zone building, for which such a grey-box model is developed and analyzed. The resulting model is rapidly developed and used in a simulation with the measured data. The outputs of the model are compared with the measured temperatures and they show good fit.

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A Resistance–Capacitance Model for Real-Time Calculation of Cooling Load in HVAC-R Systems

Cited by 21 publications

References 25 publications

Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

Identifying Grey-box Thermal Models with Bayesian Neural Networks

Detailed Thermodynamic Modeling of Multi-Zone Buildings with Resistive-Capacitive Method

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