Conference Agenda

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Session Overview
Session
Session 24: Building simulations
Time:
Thursday, 26/Aug/2021:
2:00pm - 3:30pm

Session Chair: William Stuart Dols, NIST
Location: Room 5 - Room 019, Building: 116

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Presentations
2:00pm - 2:15pm

Semi-automatic geometry modeling: faster and streamlined building simulation models

Nanna Dyrup Svane1,2, Artüras Pranskunas1, Lars Broder Lindgren2, Rasmus Lund Jensen1

1Aalborg University; 2MOE A/S

The architecture, engineering, and construction (AEC) industry experiences a growing need for building performance simulations (BPS) as facilitators in the design process. However, inconsistent modeling practice and varying quality of export/import functions entail error-prone interoperability with IFC and gbXML data formats. Consequently, repeated manual modeling is still necessary. In this paper, we present a coupling module for a semi-automated extract of geometry data from the BIM software Revit and a further translation to a BPS input file using Revit Application Programming Interface (API) and visual programming in Dynamo. The module is tested with three test cases which shows promising results for fast and structured semi-automatic geometry modeling designed to fit today's practice.



2:15pm - 2:30pm

Validation of the simplified heat conduction model of EN ISO 52016-1

Giovanna De Luca, Franz Giorgio Maria Bianco Mauthe Degerfeld, Ilaria Ballarini, Vincenzo Corrado

Politecnico di Torino, Italy

The issue of improving the building energy efficiency led to the development of calculation methods for the building energy performance assessment. To overcome the low accessibility to detailed input data, the recently introduced EN ISO 52016-1 hourly method is based on assumptions and simplifications chosen to allow a sufficient accuracy in the outcomes with a low amount of input data. Besides the general assumptions introduced by EN ISO 52017-1, a simplified mass distribution in the envelope components is considered in EN ISO 52016-1 technical standard. In particular, each opaque building element is discretised into up to five thermal R-C nodes. Moreover, an improved calculation procedure for the components’ discretisation was introduced in the Italian National Annex. However, the effects and errors related to the assumptions of the simplified methods have not been sufficiently investigated yet. The present work is thus aimed to evaluate the hypothesis of the simplified heat conduction models. The validation was performed through the comparison with the “Finite Difference” solution algorithm for 25 opaque component test cases (covering a wide range of areal heat capacity values) considering sinusoidal boundary conditions. To guarantee a general validity of the outcomes, the analysis was performed by considering both indoor and outdoor air temperatures as driving forces. Each test case was simulated with the standard and the improved algorithms and was evaluated through the comparison with the results of the “Finite Difference” solution algorithm simulations, assumed as the baseline. Despite the simplifications of the models, results reveal that both the standard and the improved algorithms allow to predict the indoor surface temperatures with an accuracy of around 0.5 °C. Future works will analyse the system response considering additional driving forces, such as internal and solar gains.



2:30pm - 2:45pm

A case study on the impact of fixed parameter values in the modelling of indoor overheating

Giorgos Petrou1, Anna Mavrogianni2, Phil Symonds2, Mike Davies2

1UCL, Energy Institute, London, United Kingdom; 2UCL, Institute of Environmental Design and Engineering, London, United Kingdom

Across multiple countries there has been a sustained effort to reduce greenhouse gas emissions from buildings while also improving their environmental resilience. To achieve these goals, building energy and thermal performance modelling plays an important part as it can inform policy makers how the current building stock will perform in the future and what the impact of different energy efficiency policies might be on energy consumption, greenhouse gas emissions and the indoor environment. To ensure robust modelling, many techniques of uncertainty propagation and calibration exist. With increasing computing power and the development of parametric tools, conducting a Monte Carlo analysis where model inputs are sampled from appropriate distributions is no longer computationally prohibitive. Nevertheless, the use fixed model inputs from reference tables is often the preferred approach to modelling buildings. Although reference tables offer an easy way of identifying model inputs, they do not capture the spread of possible values and in some instances might differ significantly from the empirically identified central value. In such cases, the reference tables can directly contribute to the perceived ‘performance gap’. A case study of the impact that the use of reference tables can have on the assessment of the indoor environment will be described in this work. Samples will be drawn from an empirical distribution of measured wall U-values and used in the simulation of an archetype model representing a group of dwellings with a similar construction type. The prevalence of indoor overheating within this group of dwellings is compared to a model where the theoretical value from a reference table is used. Through this work, we highlight not only the importance of large scale empirical studies that can inform building modelling but also the usefulness and appropriateness of probabilistic sampling, especially when looking at a group of dwellings.



2:45pm - 3:00pm

Simulating heat load profiles in buildings using mixed effects models

Jaume Palmer Real1, Jan Kloppenborg Møller1, Christoffer Rasmussen1, Karen Byskov Lindberg2, Igor Sartori2, Henrik Madsen1

1DTU, Denmark; 2SINTEF, Norway

The landscape of buildings is a diverse one and long-term energy system planning requires simulation tools that can capture such diversity. This work proposes a model for simulating the space-heating consumption of buildings using a linear mixed-effects model . This modelling framework captures the noise caused by the differences that are not being measured between individual buildings; e.g. the preferences of their occupants. The proposed model uses outdoor temperature and space-heating consumption measured at hourly resolution; thus, the model is able to predict the intra-day variations as well as longer effects. Given the stochastic nature of the simulation, the prediction interval of the simulation can be estimated, which defines a region where the consumption of any unobserved building will fall in. A whole year has been simulated and compared to out-of-sample measurements from the same period. The results show that the out-of-sample data is virtually always inside the estimated 90% prediction interval. This work uses data from Norwegian schools, although the model is general and can be built for other building categories. This amount of detail allows energy planners to draw a varied and realistic map of the future energy needs for a given location.



3:00pm - 3:15pm

Heat, Moisture and Air coupled Model for Historical Building

Guoli Zhao1, Huarong Xie1, Changchang Xia1, Shuichi Hokoi2, Yonghui Li1

1School of Architecture,Southeast University, Nanjing,China; 2School of Architecture Internationalization Demonstration, Southeast University, Nanjing, China

In the current heat and moisture numerical simulation, the indoor and outdoor air is often treated as a homogeneous state without considering the changes brought about by its flow. However, for historical building, if the impact of heat and moisture transfer on the wall caused by the indoor airflow is not considered, it may cause large errors in the simulation results. The purpose of this study is to clarify the effect of indoor airflow on the results of heat and humidity simulation. This study first established the heat, moisture and air coupling model of the building room, the SIMPLE algorithm is added to consider the indoor airflow distribution; then, the difference between the simulation results of the original model and with and without air flow is compared; finally, the correlation analysis of the heat and moisture behavior of the walls was carried out. The results show that when considering air flow, the distribution of indoor air temperature and moisture chemical potential is uneven, and the temperature and moisture content of the envelope surface fluctuate greatly, this will make the wall more prone to condensation and mold.



3:15pm - 3:30pm

Optimizing occupant-centric building controls given stochastic occupant behaviour

zeinab khorasani zadeh, Mohamed Ouf

Concordia University, Canada

Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve both occupant comfort and energy efficiency by learning and predicting occupant behaviour then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subject to several factors including uncertainty due to occupant behaviour, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings are critical to ensure optimal performance. Furthermore, identifying building design alternatives that can optimize such performance given different occupant preferences is an important step which cannot be investigated during field implementations of OCC due to logistical constraints. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic occupant behaviour models with OCCs that learn their preferences. The genetic algorithm for optimization is then used to identify the configurational settings and design parameters that minimize energy consumption under three different occupant scenarios. These scenarios represent three types of occupant, namely sensitive, tolerant, and moderate occupants which are defined based on their likelihood of interaction with building systems when feeling uncomfortable. To demonstrate the proposed framework, three OCC models to regulate lighting, heating and cooling setpoints were implemented in the building simulation program, EnergyPlus, and executed through a Python package, EPPY to optimize OCC configurational settings and design parameters. Results revealed significant improvement of OCC performance under the identified optimal configurational settings and design parameters for each of the investigated occupant scenarios. The proposed framework can be extended to other OCCs to identify optimum configurations that align with different building design alternatives and occupant types, prior to field implementations.



 
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