Intelligent BEMS design using detailed thermal simulation models and surrogate-based stochastic optimization

Kontes, Georgios; Valmaseda, Cesar; Giannakis, Georgios B.; Katsigarakis, Kyriakos; Rovas, Dimitrios

doi:10.1016/j.jprocont.2014.04.003

Cited by 24 publications

(27 citation statements)

References 14 publications

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“…After the opening volumes subtraction from their constructions, the CB surface set CB AB is obtained by applying the F cod clipping function on ∂A using the BSP-tree T B . CBI process is expressed mathematically by Equation 4.…”

Section: Common Boundary Intersection Stage -Cbi (Stage 3)mentioning

confidence: 99%

“…The recent requirement for efficient allocation of energy resources in the building sector, has resulted in the increased use of building thermal simulations, during both the building design [1,2] and operation phases [3,4]. The accuracy of a thermal simulation model strongly depends on the accurate definition of building geometric characteristics, which include: the building envelope, the building orientation, the configuration of spaces, surfaces and volumes.…”

Section: Introductionmentioning

confidence: 99%

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Automatic generation of second-level space boundary topology from IFC geometry inputs

Lilis

Giannakis

Rovas

2017

Automation in Construction

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The Industry Foundation Classes (IFC) is a semantically rich data model providing necessary information to support extraction of information necessary for the setup of building energy simulations. Often, 2nd -level space boundary data contained in IFC, are missing or incorrect. To facilitate the connection between BIMs and energy simulation programs, the Common Boundary Intersection Projection (CBIP) algorithm is introduced. CBIP uses the geometric representations of building entities obtained from IFC files to generate the building's 2 nd -level space boundary topology. A prototypical implementation of the CBIP algorithm is used in a complex geometry building, as a verification of the capability of the algorithm to identify space boundaries.

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Section: Common Boundary Intersection Stage -Cbi (Stage 3)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic generation of second-level space boundary topology from IFC geometry inputs

Lilis

Giannakis

Rovas

2017

Automation in Construction

Self Cite

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“…For both cases, the air and operative temperatures coincide, due to the dynamics of the system and the heavy construction and insulation of the building. On the other hand, Figure 16b reveals the well-known overheating problem of buildings equipped with TABS due to internal and solar gains [39,61], indicating the utilization of solutions facilitating weather and occupancy forecasts [2,16,39] as an attractive option for control. …”

Section: Zub Buildingmentioning

confidence: 99%

Using Thermostats for Indoor Climate Control in Office Buildings: The Effect on Thermal Comfort

et al. 2017

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Thermostats are widely used in temperature regulation of indoor spaces and have a direct impact on energy use and occupant thermal comfort. Existing guidelines make recommendations for properly selecting set points to reduce energy use, but there is little or no information regarding the actual achieved thermal comfort of the occupants. While dry-bulb air temperature measured at the thermostat location is sometimes a good proxy, there is less understanding of whether thermal comfort targets are actually met. In this direction, we have defined an experimental simulation protocol involving two office buildings; the buildings have contrasting geometrical and construction characteristics, as well as different building services systems for meeting heating and cooling demands. A parametric analysis is performed for combinations of controlled variables and boundary conditions. In all cases, occupant thermal comfort is estimated using the Fanger index, as defined in ISO 7730. The results of the parametric study suggest that simple bounds on the dry-bulb air temperature are not sufficient to ensure comfort, and in many cases, more detailed considerations taking into account building characteristics, as well as the types of building heating and cooling services are required. The implication is that the calculation or estimation of detailed comfort indices, or even the use of personalised comfort models, is key towards a more human-centric approach to building design and operation.

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“…To cope with the increased computational burden a surrogate‐based optimization approach has been developed, outlined in Section and described in more detail in Kontes et al. ().…”

Section: The Sta Frameworkmentioning

confidence: 99%

“…Let

I = [0, T_{p}]

be the look‐ahead period for which we want to generate optimized control decisions; without loss of generality

t = 0

is the time when the control design starts, and

t = T_{p}

the prediction horizon. We introduce a partition

scriptI

of I in N intervals:

I = {I_{k} = [t_{k - 1}, t_{k}] | k \in {1, ..., N}, \cup_{k = 1}^{N} I_{k} = I}

with k a discrete‐time index (Kontes et al., ). The discrete‐time model of the system, which in our case is a detailed thermal simulation model capturing all relevant dynamics, is assumed to be given by the following nonlinear equation:

x_{k + 1} = m (x_{k}, u_{k}, d_{k})

…”

Section: The Sta Frameworkmentioning

confidence: 99%

Sense‐Think‐Act Framework for Intelligent Building Energy Management

Katsigarakis

Kontes

Giannakis

et al. 2015

Computer aided Civil Eng

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The realization of smart and energetically efficient buildings is contingent upon the successful implementation of two tasks that occur on distinct phases component. An illustrative application of the proposed methodology in an office building is provided. MOTIVATIONEffective utilization of energy in buildings is receiving significant attention. This interest is justified on the observation that buildings account for a significant portion of end-energy use: in Europe 40% of the total energy consumed is used for the operation of buildings (Perez-Lombard et al., 2008) with a significant part of that energy used for conditioning occupied spaces. Energy retrofits, properly selected and executed, can yield appreciable reduction in energy demands. But the value of effective energy utilization during the building operational phase is undisputed both in terms of achieving good occupant comfort and in reducing energy consumption.In domestic environments the extensive research on intelligent temperature regulation and the proliferation of smart thermostats and home automation solutions are partly a response to that need. In the nondomestic building sector Building Energy Management Systems (BEMS) are used to ensure proper operation of systems and components in an energyefficient manner. Typical BEMS installations comprise a communication and data management layer enabling communication and data transfer between interacting field devices (sensors, actuators, field controllers); C 2015 Computer-Aided Civil and Infrastructure Engineering.

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Intelligent BEMS design using detailed thermal simulation models and surrogate-based stochastic optimization

Cited by 24 publications

References 14 publications

Automatic generation of second-level space boundary topology from IFC geometry inputs

Automatic generation of second-level space boundary topology from IFC geometry inputs

Using Thermostats for Indoor Climate Control in Office Buildings: The Effect on Thermal Comfort

Sense‐Think‐Act Framework for Intelligent Building Energy Management

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