Building Energy Performance Simulation Tools Research Articles

Identification of Environmental and Contextual Driving Factors of Air Conditioning Usage Behaviour in the Sydney Residential Buildings

Air conditioning (A/C) is generally responsible for a significant proportion of total building energy consumption. However, occupants’ air conditioning usage patterns are often unrealistically characterised in building energy performance simulation tools, which leads to a gap between simulated and actual energy use. The objective of this study was to develop a stochastic model for predicting occupant behaviour relating to A/C cooling and heating in residential buildings located in the Subtropical Sydney region of Australia. Multivariate logistic regression was used to estimate the probability of using A/C in living rooms and bedrooms, based on a range of physical environmental (outdoor and indoor) and contextual (season, day of week, and time of day) factors observed in 42 Sydney region houses across a two-year monitoring period. The resulting models can be implemented in building energy performance simulation (BEPS) tools to more accurately predict indoor environmental conditions and energy consumption attributable to A/C operation.

A Comparative Study of Artificial Intelligence Models for Predicting Interior Illuminance

ABSTRACT Thanks to the recent advances in computer technology, many building energy performance simulation tools have been developed in the current market. Designers and architects are interested in working on this topic in the early phases of the project. However, effective energy solutions are computationally expensive. As a result, having a comprehensive insight into the project conditions in the early phases of the work is a vital issue. The present study aimed to propose an artificial intelligence (Al) model to generate a reasonably accurate estimate in a short time. To this end, four machine learning models and one artificial neural network (ANN) are selected and their results are compared to assess their capabilities in energy performance estimation. This study investigates the influence of the exterior louver design on the interior energy performance of a structure. A specific dataset is generated and tested on four powerful regression models (i.e., polynomial Linear Regression, Random Forests (RF), Decision Tree (DT), and Support Vector Regression (SVR)) and one Artificial Neural Network (ANN). Finally, a comparative analysis is presented. The findings of this research support the use of machine learning tools and ANNs as a convenient and accurate strategy for predicting building parameters.

Opportunities and Limitations of Building Energy Performance Simulation Tools in the Early Stages of Building Design in the UK

This research investigates the use of Building Energy Performance Simulation (BEPS) tools in the early stages of building design in UK architectural practices with a particular focus on the barriers and opportunities to their effective application and further uptake. Two primary methods of investigation were undertaken; the first was a wide survey among UK architects and architectural practices, where the responses of 418 participants were electronically gathered and analysed. A deeper understanding of the issues was developed through an analysis of the process of low-energy building design using semi-structured interviews with six representatives of well-established architectural practices in the UK. The findings reveal that while there is an increasing understanding of the importance of BEPS involvement at the early design stages, there was limited evidence of actual early implementation of BEPS tools other than a few well known but specialised tools such as Passive House Planning Package (PHPP) which were used mainly for domestic energy performance evaluation. However, many practices surveyed showed interest in achieving higher standards than the “basic” regulatory backstops, which has resulted in Passive House and BREEAM seeing increasing use in domestic and non-domestic projects respectively. Although there has been a significant increase in the development and availability of tools and methodologies for assessing building energy performance our study shows that the focus for future research needs to be shifted from the “tool development” to looking at methods of implementing and using such tools in practice at the early design stages.

The impact of thermophysical properties and hysteresis effects on the energy performance simulation of PCM wallboards: Experimental studies, modelling, and validation

This paper presents a combined experimental and numerical procedure, developed to test the thermophysical behaviour of a real scale PCM wallboard, aiming at providing reliable data for the validation of building energy performance simulation tools. Data obtained from two experimental tests, conducted on real building elements integrating PCM undergoing complete and incomplete phase change, are considered for assessing the impact of thermal properties and hysteresis. A comprehensive comparative numerical analysis, performed by means of an in-house developed simulation tool, called DETECt, is carried out to investigate the reliability of several modelling and simulation approaches available in literature.Monitored data are used to compare the reliability of several modelling and simulation approaches (three different specific heat curves and four modelling approaches for hysteresis) to find out the appropriate temperature dependent heat capacity curves which realistically describe the PCM performance. ResultsThe use of cp – temperature curves characterized through thermophysical analysis (e.g. DSC) under conditions much different than those that the PCM can undergo during the operation of a building can lead to significant discrepancies in performances with the actual data. Moreover, in case of incomplete phase transitions, the lower the accuracy of the cp – temperature curves, the higher the influence of the hysteresis modelling approach on the reliability of simulation results; a better agreement between monitored data and simulation results is observed when hysteresis is modelled by considering the transition between heating and cooling (and vice versa), by switching from the melting to the solidification curves according to the liquid and solid mass fraction.The study shows that the more refined modelling of the phase change allows results to be consistent with the thermo-physics phenomena of composite PCM under real conditions and achieve more reliable results.

MVD based information exchange between BIM and building energy performance simulation

Abstract The process of preparing building energy performance simulation (BEPS) models involves repetitive manual operations that often lead to data losses and errors. As a result, BEPS model inputs can vary widely from this time consuming, non-standardised and subjective process. This paper proposes a standardised method of information exchange between Building Information Modelling (BIM) and BEPS tools using the Information Delivery Manual (IDM) and Model View Definition (MVD) methodologies. The methodology leverages a collection of use cases to initiate the identification of exchange requirements needed by BEPS tools. The IDM/MVD framework captures and translates exchange requirements into the Industry Foundation Classes (IFC) schema. The suggested approach aims to facilitate the transfer of information from IFC based BIM to either conventional or advanced BEPS tools (e.g. EnergyPlus and Modelica) through the development of a specific MVD that defines a subset of the IFC data model that deals with building energy performance simulation. By doing so, the potential of BIM-based simulation can be fully unlocked, and a reliable and consistent IFC subset is provided as an input for energy simulation software.

Integration time step issue in Mediterranean Historic Building energy simulation

The European target towards Zero Energy buildings is focusing on the energy retrofit of existing buildings. Often, in Mediterranean countries, existing buildings have historical relevancies, involving constraints, when dealing with their energy renovation, due to conservation reasons. To analyze possible energy retrofit actions, building energy performance simulation tools (BEPSt) are valuable. However, old buildings are often made of large stone walls whose resolution through conduction transfer function (CTF) used for sub-hourly time step simulation might be critical. This paper analyses how EnergyPlus and TRNSYS address such problem, explaining the differences between them and the reasons for low quality results.

Evaluation of Performance Modelling: Optimizing Simulation Tools to Stages of Architectural Design

Given the necessity of resilience architecture is vital, the most effective decisions on building performance are made at design stages. Simulation represents a possible solution to the complex problem of enabling comprehensive and integrated appraisals of design options under realistic operation conditions [1]. Building Energy Performance Simulation tools (BEPS) provide an opportunity for architects to assess their design strategies in order to reach a high performance building. According to the current developments of Building Information Modeling (BIM) as an Integrated Project Delivery (IPD) tool this study tries to define a logical relation between level of development in different stages of architectural design and BEPS tools in order to fill the gap between architects and engineers in earlier stages of building design. In order to assess the current capability of the simulation tools and evaluate their platforms as an Integration of tools in building design decision making by defined criteria of Input data required for simulation, Usability and graphical visualization of the interface, Interoperability of building modelling, Accuracy and ability to simulate detailed and complex building components. In order to have a market assessment of these types of building performance tools, a survey was conducted of architectural and engineering firms of diverse size.

EnergyPlus as a forensic tool: Thermal reconstruction of a crime scene using calibrated simulation

This study utilized energy simulation in support of a forensic pathology time-of-death analysis for a corpse discovered in a single-family residence two years prior to the study. In order to produce an accurate estimate of the interior temperature profile at the time of death, a thermal model was constructed using EnergyPlus and calibrated using environmental monitoring data from the site. The calibration methods used in the study draw from several precedents and are presented in detail. The thermal model was able to predict the temperature in the room of interest within 1.4°C (2.5°F) with 90% confidence. This model was then altered to account for known differences between the monitoring period and the period of interest, and used to predict what the temperature profile had been at the time of death. This study adds to a small body of work that compares simulated to measured performance data for unconditioned spaces, which should have a growing relevance as building energy performance simulation tools are used to model passive strategies.

EPAR: Energy Performance Augmented Reality models for identification of building energy performance deviations between actual measurements and simulation results

Building energy performance simulation tools such as EnergyPlus, Ecotect, and eQuest are widely used to model energy performance of existing buildings and assess retrofit alternatives. Nevertheless, predictions from simulations typically deviate from actual measurements. Monitoring actual performance and measuring deviations from simulated data in 3D can help improve simulation accuracy through model calibrations, and in turn facilitate identification of energy performance problem. To do that, this paper presents Energy Performance Augmented Reality (EPAR) modeling that leverages collections of unordered digital and thermal imagery, in addition to computational fluid dynamics (CFD) models. First, users collect large numbers of digital and thermal imagery from the building under inspection using a single thermal camera. Through an image-based reconstruction pipeline, actual 3D spatio-thermal models are automatically generated and are superimposed with expected building energy performance models generated using CFD analysis through a user-driven process. The outcomes are EPAR models which visualize actual and expected models in a common 3D environment. Within the EPAR models, actual measurements and simulated results can be systematically compared and analyzed. The method is validated on typical residential and instructional buildings. The results demonstrate that EPAR models facilitate calibration of building energy performance models and support detection and analysis of building performance deviations.

Building modeling as a crucial part for building predictive control

Recent results show that a predictive building automation can be used to operate buildings in an energy and cost effective manner with only a small retrofitting requirements. In this approach, the dynamic models are of crucial importance. As industrial experience has shown, modeling is the most time-demanding and costly part of the automation process. Many papers devoted to this topic actually deal with modeling of building subsystems. Although some papers identify a building as a complex system, the provided models are usually simple two-zones models, or extremely detailed models resulting from the use of building simulation software packages. These are, however, not suitable for predictive control. The objective of this paper is to share the years-long experience of the authors in building modeling intended for predictive control of the building's climate. We provide an overview of identification methods for buildings and analyze their applicability for subsequent predictive control. Moreover, we propose a new methodology to obtain a model suitable for the use in a predictive control framework combining the building energy performance simulation tools and statistical identification. The procedure is based on the so-called co-simulation that has appeared recently as a feature of various building simulation software packages.