EXPLORING THE VALIDITY OF OCCUPANT BEHAVIOR MODEL FOR IMPROVING OFFICE BUILDING ENERGY SIMULATION

Author(s):  
Mengda Jia ◽  
Ravi S. Srinivasan ◽  
Robert Ries ◽  
Gnana Bharathy
2019 ◽  
Vol 85 ◽  
pp. 01007
Author(s):  
Ioana Udrea ◽  
Romeo Popa

An early phase design exercise for shading a South facade of an office building in Bucharest is presented here. The problem to solve is deciding in a simple and quick way (not using the complicated BSDF approach), based strictly on energy-efficiency considerations, between two options in principle: the first, exterior screens, is much cheaper and the send is unmovable horizontal aluminium slats. The tool used to produce the necessary result quantities by building energy simulation is COMFEN 4.1. The conclusion is positive: if aesthetic reasons are ignored, in Bucharest and very likely many other Romanian cities having a quite similar climate, screens can be at least equally effective in saving energy by South facade shading. As they allow a flexible shading strategy (removing them during some months of the heating season), the energy-efficiency realized by having them on throughout the year can be increased further.


2020 ◽  
Vol 12 (10) ◽  
pp. 4086 ◽  
Author(s):  
Mengda Jia ◽  
Ravi Srinivasan

Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture static features of a building including the envelope, geometry, and Heating, Ventilation, and Air Conditioning (HVAC) systems, etc. However, building performance also relies on dynamic features such as occupants’ interactions with the building. Such interactions have not been fully implemented in building energy simulation tools, which potentially influences the comprehensiveness and accuracy of estimations. This paper discusses an information exchange mechanism via coupling of EnergyPlus™, a building energy simulation engine and PMFServ, an occupant behavior modeling tool, to alleviate this issue. The simulation process is conducted in Building Controls Virtual Testbed (BCVTB), a virtual simulation coupling tool that connects the two separate simulation engines on a time-step basis. This approach adds a critical dimension to the traditional building energy simulation programs to seamlessly integrate occupants’ interactions with building components to improve the modeling capability, thereby improving building performance evaluation. The results analysis of this paper reveals a need to consider metrics that measure different types of comfort for building occupants.


Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 22 ◽  
Author(s):  
Bárbara Torregrosa-Jaime ◽  
Pedro J. Martínez ◽  
Benjamín González ◽  
Gaspar Payá-Ballester

Variable refrigerant flow (VRF) systems are one possible tool to meet the objective that all new buildings must be nearly zero-energy buildings by 31 December 2020. Building Information Modelling (BIM) is a methodology that centralizes building construction project information in a digital model promoting collaboration between all its agents. The objectives of this work were to develop a more precise model of the VRF system than the one available in EnergyPlus version 8.9 (US Department of Energy) and to study the operation of this system in an office building under different climates by implementing the building energy simulation in an Open BIM workflow. The percentage deviation between the estimation of the VRF energy consumption with the standard and the new model was 6.91% and 1.59% for cooling and heating respectively in the case of Barcelona and 3.27% and 0.97% respectively in the case of Madrid. The energy performance class of the analysed building was A for each climatic zone. The primary energy consumption of the office building equipped with the VRF system was of 65.8 kWh/(m2·y) for the Mediterranean climate of Barcelona and 72.4 kWh/(m2·y) for the Continental climate of Madrid.


Data in Brief ◽  
2021 ◽  
pp. 107370
Author(s):  
Mara Magni ◽  
Fabian Ochs ◽  
Samuel de Vries ◽  
Alessandro Maccarini ◽  
Ferdinand Sigg

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