Building thermal model reduction using nonlinear constrained optimization

Gouda, M. M.; Danaher, S.; Underwood, Chris

doi:10.1016/s0360-1323(01)00121-4

Cited by 192 publications

(130 citation statements)

References 14 publications

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“…In this paper, each room (zone) is considered as a single capacitance and each wall is represented with 3 resistors and 2 capacitors also referred to as the 3R2C modeling approach [11]. Fig.…”

Section: Modeling a Lumped Parameter Modeling Of Buildings Usingmentioning

confidence: 99%

“…A commonly used method of modeling the thermal dynamics of buildings is using lumped resistive-capacitive networks [10], [11], [12]. In this paper, each room (zone) is considered as a single capacitance and each wall is represented with 3 resistors and 2 capacitors also referred to as the 3R2C modeling approach [11].…”

Section: Modeling a Lumped Parameter Modeling Of Buildings Usingmentioning

confidence: 99%

See 1 more Smart Citation

Learning/repetitive control for building systems with nearly periodic disturbances

Vinther

Chandan

Alleyne

2013

2013 European Control Conference (ECC)

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Abstract-In this paper, learning/repetitive control is proposed for improvement of existing feedback control loops for temperature regulation in buildings. A single zone office building is used as an example, with real weather data for Phoenix Arizona and realistic occupancy load schedules. Simulations have shown a decrease in the average set point tracking error of more than 50%, even without additional energy consumption. This can be achieved in situations where the load disturbances have enough repeatability and a repeatable-to-nonrepeatable ratio can be computed to determine if learning should be used and at which frequencies. Furthermore, the increased tightness in reference tracking could be used to lower energy consumption by moving the reference set point closer to the boundaries of the allowable temperature range.

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Section: Modeling a Lumped Parameter Modeling Of Buildings Usingmentioning

confidence: 99%

Section: Modeling a Lumped Parameter Modeling Of Buildings Usingmentioning

confidence: 99%

Learning/repetitive control for building systems with nearly periodic disturbances

Vinther

Chandan

Alleyne

2013

2013 European Control Conference (ECC)

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“…Constraints (6) and (7) The most involved constraints in the HVAC model come from modeling building thermal dynamics. We use a lumped resistor-capacitor (RC) network, which is commonly used in [7][8][9] for constructing a model of the transient heat flow through solid surfaces, such as walls and windows. To predict zone temperatures we use the following parameters as inputs: (1) characteristics of the supply air (flow rate and temperature), (2) thermal heat gain due to occupants, (3) thermal heat gain due to solar radiation, and (4) outdoor temperature.…”

Section: Hvac Modelmentioning

confidence: 99%

Large Neighborhood Search for Energy Aware Meeting Scheduling in Smart Buildings

Lim

Briel

Thiébaux

et al. 2015

Integration of AI and OR Techniques in Constraint Programming

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Abstract. One of the main inefficiencies in building management systems is the widespread use of schedule-based control when operating heating, ventilation and air conditioning (HVAC) systems. HVAC systems typically operate on a pre-designed schedule that heats or cools rooms in the building to a set temperature even when rooms are not being used. Occupants, however, influence the thermal behavior of buildings. As a result, using occupancy information for scheduling meetings to occur at specific times and in specific rooms has significant energy savings potential. As shown in Lim et al. [15], combining HVAC control with meeting scheduling can lead to substantial improvements in energy efficiency. We extend this work and develop an approach that scales to larger problems by combining mixed integer programming (MIP) with large neighborhood search (LNS). LNS is used to destroy part of the schedule and MIP is used to repair the schedule so as to minimize energy consumption. This approach is far more effective than solving the complete problem as a MIP problem. Our results show that solutions from the LNS-based approach are up to 36% better than the MIP-based approach when both given 15 minutes.

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“…On the other hand, data driven models [18][19][20][21][22][23][24][25][26][27][28] are based solely on measurements and are typically identified without information on the physical nature of the building properties. Hybrid or grey box models are a combination of data driven and physical modelling approaches [29][30][31][32][33][34][35]. This model type needs sufficient amount of measurement data for parameter identification and some information about the properties of the building.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Model for Indoor Temperature Prediction in Buildings

2018

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A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.

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Building thermal model reduction using nonlinear constrained optimization

Cited by 192 publications

References 14 publications

Learning/repetitive control for building systems with nearly periodic disturbances

Learning/repetitive control for building systems with nearly periodic disturbances

Large Neighborhood Search for Energy Aware Meeting Scheduling in Smart Buildings

A Dynamic Model for Indoor Temperature Prediction in Buildings

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