Performance Simulation Tools Research Articles

Comparison of wooden and conventional houses sustainability: Increasing application of modified wood in R. of Macedonia

The residential sector in Republic of North Macedonia, situated in south-east Europe, is responsible for the consumption of significant amounts of resources and for the production of large amount of emissions and waste. The increased application of wood products can substantially improve these conditions and contribute towards increasing the sustainability in the construction industry and the creation of sustainable homes. The contribution of this paper is the simulation of four different alternatives of residential buildings in the Republic of North Macedonia, evaluated in terms of energy performance and life-cycle assessment for the "cradle to gate" phase. The results of this study revealed that by replacing conventional concrete and masonry constructions with wooden constructions in low-rise family houses, the carbon emissions can be reduced up to 145%. The contribution of this paper is the simulation and analysis of the energy performance by using building performance simulation tools and life-cycle assessment of a residential building and its optimization through several models. The results give significant insight on the influence that the different construction materials have on the environment and buildings performance. Also, the research enables stimulation of the construction industry in utilizing wooden structures and delivering legislation that could increase their use. These actions would provide means for the development of sustainable buildings, neighborhoods and sustainable development of the Republic of North Macedonia.

Impact of IPPC Scenarios on indoor environment quality of historic buildings

Ensuring proper indoor environment quality in buildings with historic value or buildings located in historic centres of cities is not an easy task. These buildings are frequently listed in historic preservation lists; thus, the amount of possible refurbishment methods is significantly limited due to increased protection. This article deals with comprehensive analysis of internal microclimate of a multi-purpose building located in the historic centre of Prague during summer period. Possible refurbishment methods permitted by the National Heritage Institute are analysed and compared using building energy performance simulation tool BSim in order to achieve proper working conditions in offices in the building. Structural and technical modifications are proposed in order to optimize the amount of solar heat gains leading to reduction of overheating and increase of energy efficiency. Furthermore, two global warming projections from the Fourth Assessment Report of Intergovernmental Panel on Climate Change are applied to the current weather data to examine the impact of the global climate change on the building. As expected, the cooling demand increases with the climate change scenarios presenting more difficult challenges to maintain the indoor environment quality within the limitations given by the legislation.

Namenski geometrijski dizajn zastakljivanja za analizu energetske efikasnosti zgrada

Geometry is an important architectural design issue in the assessment of a building model's energy performance using Building Performance Simulation tools. The commonly used window-to-wall ratio method, while providing a fast and automatic way of modelling glazing, is prone to geometrical inaccuracies, which can contribute to the energy performance gap between modelled and monitored buildings. To alleviate these challenges, this paper presents a mechanism for creating a bespoke glazing design on curved surfaces based on the concept of UV-mapping. The glazing can be designed on a 2D planar vector drawing as a set of interconnected curves, which are then mapped unto the UV space of the subdivided and planarised input wall as the glazing. It is hypothesised that a building model with a bespoke glazing design, while more time consuming, allows a more aesthetically representative and geometrically accurate glazing design, thus minimising the energy performance gap.

Regarding Solid Oxide Fuel Cells Simulation through Artificial Intelligence: A Neural Networks Application

Because of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8YSZ/LSCF anode supported planar button cells. The best simulator obtained is a 3-hidden layer ANN (25/22/18 neurons per layer, hyperbolic tangent sigmoid as transfer function, obtained with a gradient descent with adaptive learning rate backpropagation). This shows high accuracy (RMS = 0.67% in the testing phase) and successful application in the forecast of SOFC polarization behaviour in two additional experiments (RMS in the order of 3% is scored, yet it is reduced to about 2% if only the typical operating current density range of real application is considered, from 300 to 500 mA·cm−2). Therefore, the neural tool is suitable for system simulation codes/software whether SOFC operating parameters agree with the input ranges (anode feeding composition 0–48%vol H2, 0–38%vol CO, 0–45%vol CH4, 9–32%vol CO2, 0–54%vol N2, specific equivalent hydrogen flow-rate per unit cell active area 10.8–23.6 mL·min−1·cm−2, current density 0–1300 mA·cm−2 and temperature 700–800 °C).

An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica

Abstract The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers' poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts' involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components' control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems.

Computational performance analysis of overheating mitigation measures in parked vehicles

Parked vehicles have the tendency to overheat quickly. This can lead to a negative impact on the thermal comfort of the driver and its passengers, as well as intensive use of air conditioning systems, and thus fuel consumption of the vehicle or, in the case of electric mobility, a reduced cruising range. In the search for effective measures to mitigate this effect, important guidance can be provided by the field of sustainable building design. On the one hand, inspiration can come from design strategies in terms of shapes and advanced cover materials, but this paper advocates that this can also pertain to the simulation-based design support tools that are used by building engineers. This paper first presents the results of a thermal soak test, and then uses this data to demonstrate the suitability of the building performance simulation tool EnergyPlus for predicting the thermal behavior of parked vehicles. This fit-for-purpose validated model is used to evaluate the performance of three overheating mitigation measures for two car models in two climates. The results show that spectrally selective glazing can reduce the cabin air temperature by 12.5 °C and when combined with solar reflective opaque surfaces, the reduction of cabin air temperature can reach 23.8 °C. Increased use of building performance simulation in the automotive domain can help to further optimize the overheating reduction potential of cars.

A new approach to performance-based building design exploration using linear inverse modeling

Despite the development of a large number of building performance simulation tools, designers still need a systematic framework appropriate for energy-oriented decision-making in the early stages of design. While the current workflow follows a “forward” modelling procedure in which simulation tools predict the performance of a design, this study proposes an “inverse” procedure that entails a performance objective that estimates design parameters. Using linear inverse modelling, this approach generates plausible ranges for design parameters given a preferred thermal performance. The paper begins by demonstrating that thermal demand in a particular building operation-and-climate condition can be expressed as a linear regression model and then, in two case-studies, uses the regression model to develop an inverse algorithm. After defining energy performance targets as input, users obtain a probabilistic estimate of design parameters as output that represents a large “menu” of feasible design solutions, provides confidence, and embodies the iterative nature of design.

Can the choice of building performance simulation tool significantly alter the level of predicted indoor overheating risk in London flats?

The accurate prediction of building indoor overheating risk is critical in order to mitigate its possible consequences on occupant health and wellbeing. The Chartered Institution of Building Services Engineers issued Technical Memorandum 59 (TM59) with the aim of achieving consistency in the modelling processes followed for the prediction of overheating risk in new dwellings. However, as each tool’s prediction may depend on its inherent assumptions, an inter-model comparison procedure was used to assess whether the choice of building performance simulation tool influences the overheating assessment. The predictions of two popular tools, IES VE and EnergyPlus, were compared for nine variations of a naturally ventilated, purpose built, London flat archetype, modelled under the default algorithm options. EnergyPlus predicted a high overheating risk according to TM59 criteria in seven out of the nine model variants, contrary to the low risk of all the IES VE variants. Analysis of heat transfer processes revealed that wind-driven ventilation and surface convection algorithms were the main sources of the observed discrepancies. The choice of simulation tool could thus influence the overheating risk assessment in flats, while the observed discrepancies in the simulation of air and heat transfer could have implications on other modelling applications. Practical application:Technical Memorandum 59 issued by the Chartered Institution of Building Services Engineers may be widely adopted within the industry to assist the prediction of overheating risk in new dwellings. This work suggests that the choice of building performance simulation tool can greatly influence the predicted overheating risk. Furthermore, the differences identified in the modelling of heat transfer processes could also impact other modelling applications. Following these results, the need for detailed empirical validation studies of naturally ventilated homes has been highlighted.

Energy and comfort performance benefits of early detection of building sensor and actuator faults

This paper presents a building performance simulation-based investigation to better understand the energy and comfort performance benefits of early detection of common sensor and actuator faults. Five types of air-handling unit faults and four types of zone-level faults were implemented to the energy management system application of the building performance simulation tool EnergyPlus. During 50-year simulation periods, the faults were randomly permitted to affect 75 different components of an archetype medium-sized office building model. The sensitivity of the simulation results with respect to three variables was studied: fault recurrence period, fault repair period, and discomfort threshold for simulated complaints. The results indicate that the energy use intensity and the predicted percentage of dissatisfied exhibit a power–law relationship with time, in which most of the performance reductions occur in the first 10 years. If the work-orders are issued only upon occupant complaints, the faults were estimated to cause a 16–62% increase in the energy use intensity for heating, ventilation, and air-conditioning and a 11–38% increase in the predicted percentage of dissatisfied at the end of the 50-year simulation periods. The results indicate that if the faults can be detected within a month after their first appearance, almost all their detrimental effects on a building’s energy and comfort performance can be mitigated. Practical application: The methodology and results presented in this article are of practical use for those who study on-going commissioning, fault detection and diagnostics, and energy management systems in buildings. The simulation-based parametric analysis approach can be used to estimate the range of energy and comfort savings expected through early detection of common sensor and actuator faults in commercial buildings. Insights gathered from such an analysis can be used in planning the frequency of retro-commissioning and investments for automated fault detection and diagnostics systems.

Improving occupant-related features in building performance simulation tools

Despite the research advances and demonstrated benefit of occupant modelling and simulation in recent years, its uptake in building simulation practice has been relatively slow. One of the underlying causes of this issue is limited occupant-related features of building performance simulation (BPS) tools. To this end, we present a detailed breakdown of occupant-related features and compare them between common BPS tools. Based on the outcomes of a stakeholder workshop, and an international survey that focused on occupant modelling, we provide detailed recommendations to improve occupant-related features in BPS tools. We finally present a case study demonstrating the suggested occupant-related features to apply multiple occupancy assumptions, and integrate occupant behaviour models from the literature. Results are presented as part of a non-functional mock-up graphical user interface (GUI) to demonstrate potential features of BPS tools given the suggested occupant-related improvements. These suggested improvements for BPS tools would enable users to quickly assess proposed designs’ sensitivity to different occupancy scenarios, and ultimately increase the robustness of their final designs. The presented recommendations are relevant to practitioners, researchers, and BPS tool developers as part of the efforts to increase the uptake of detailed occupant modelling in building simulation practice.

Intelligent co-simulation: neural network vs. proper orthogonal decomposition applied to a 2D diffusive problem

One possibility to improve the accuracy of building performance simulation (BPS) tools is via co-simulation techniques, where more accurate mathematical models representing particular and complex physical phenomena are employed through data exchanging between the BPS and a specialized software where those models are available. This article performs a deeper investigation of a recently proposed co-simulation technique that presents as novelty the employment of artificial intelligence as a strategy to reduce the computational burden generally required by co-simulations. Basically, the strategy, known as intelligentco-simulation, constructs new mathematical models through a learning procedure (training period) that is performed using the input–output data generated by a standard co-simulation, where the models of specialized software are employed. Once the learning phase is complete, the specialized software is disconnected from the BPS and the simulation goes on by using the synthesized models, requiring a much lower computational cost and with a low impact on the accuracy of the results. The synthesis of accurate-and-fast models is performed through machine learning techniques and the purpose of this paper is precisely a deep investigation of two techniques – recurrent neural networks and proper orthogonal decomposition reduction method, whose main goal is to reduce the training time period and to improve the accuracy. The case study focuses on a co-simulation between Domus and CFX programs, performing a two-dimensional diffusive heat transfer problem through a building envelope. The results show that for a standard co-simulation of 14 h, the intelligent co-simulation provided a reduction of 90% in the computer run time with accuracy error at the order of .

Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling

We provide a detailed estimate of the technical potential of rooftop solar photovoltaic (PV) electricity generation throughout the contiguous United States. This national estimate is based on an analysis of select US cities that combines light detection and ranging (lidar) data with a validated analytical method for determining rooftop PV suitability employing geographic information systems. We use statistical models to extend this analysis to estimate the quantity and characteristics of roofs in areas not covered by lidar data. Finally, we model PV generation for all rooftops to yield technical potential estimates. At the national level, 8.13 billion m2 of suitable roof area could host 1118 GW of PV capacity, generating 1432 TWh of electricity per year. This would equate to 38.6% of the electricity that was sold in the contiguous United States in 2013. This estimate is substantially higher than a previous estimate made by the National Renewable Energy Laboratory. The difference can be attributed to increases in PV module power density, improved estimation of building suitability, higher estimates of total number of buildings, and improvements in PV performance simulation tools that previously tended to underestimate productivity. Also notable, the nationwide percentage of buildings suitable for at least some PV deployment is high—82% for buildings smaller than 5000 ft2 and over 99% for buildings larger than that. In most states, rooftop PV could enable small, mostly residential buildings to offset the majority of average household electricity consumption. Even in some states with a relatively poor solar resource, such as those in the Northeast, the residential sector has the potential to offset around 100% of its total electricity consumption with rooftop PV.

An artificial intelligence-based method to efficiently bring CFD to building simulation

A computational procedure known as co-simulation has been proposed in the literature as a possibility to extend the capabilities and improve the accuracy of building performance simulation (BPS) tools. Basically, the strategy relies on the data exchanging between the BPS and a specialized software, where specific physical phenomena are simulated more accurately thanks to a more complex model, where advanced physics are taken into account. Among many possibilities where this technique can be employed, one could mention airflow, three-dimensional heat transfer or detailed HVAC systems simulation, which are commonly simplified in BPS tools. When considering complex models available in specialized software, the main issue of the co-simulation technique is the considerable computational effort demanded. This paper proposes a new methodology for time-consuming simulations with the purpose of challenging this particular issue. For a specific physical phenomenon, the approach consists of designing a new model, called prediction model, capable to provide results, as close as possible to the ones provided by the complex model, with a lower computational run time. The synthesis of the prediction model is based on artificial intelligence, being the main novelty of the paper. Basically, the prediction model is built by means of a learning procedure, using the input and output data of co-simulation where the complex model is being used to simulate the physics. Then the synthesized prediction model replaces the complex model with the purpose of reducing significantly the computational burden with a small impact on the accuracy of the results. Technically speaking, the learning phase is performed using a machine learning technique, and the model investigated here is based on a recurrent neural network model and its features and performance are investigated on a case study, where a single-zone house with a triangular prism-shaped attic model is co-simulated with both CFX (CFD tool) and Domus (BPS tool) programs. Promising results lead to the conclusion that the proposed strategy enables to bring the accuracy of advanced physics to the building simulation field – using prediction models – with a much reduced computational cost. In addition, re-simulations might be run solely with the already designed prediction model, demanding computer run times even lower than the ones required by the lumped models available in the BPS tool.

Heat transfer through window frames in EnergyPlus: model evaluation and improvement

ABSTRACTWindow frames can significantly affect the energy demand of buildings. Due to their complex non-planar geometry, two-dimensional, time-consuming simulations are necessary to estimate with precision heat transfer through frames. However, in dynamic heat transfer calculation through computer-based building performance simulation tools, simplifications are made to reduce simulation time. In particular, EnergyPlus models window frames as rectangular profiles, and uses a one-dimensional heat transfer model. In order to evaluate whether this simplification is legitimate, thermal transmittance values were calculated for a selection of frames, on one hand through two-dimensional finite elements simulation, and on the other hand, through a spreadsheet that reproduces the steps of the algorithm currently used by EnergyPlus to calculate heat transfer through window frames. The comparison of both values showed relative errors higher than 20%, including for rectangular frames (without simplifying geometry). Aluminium frames without thermal break exhibited the highest absolute errors, up to 2.98 W/m2K. An alternative algorithm was proposed, which uses compactness factors for film coefficient calculation and reduced emissivities to take into account self-viewing surfaces. The proposed model improved the estimates, reducing the absolute error to less than 0.10 W/m2K in 52% of the occurrences, and to less than 0.47 W/m2K for aluminium windows.

3D internal forced convection heat-transfer correlations from CFD for building performance simulation

This study presents new correlations for internal forced convective heat-transfer coefficients (CHTCs) in buildings. CHTCs are used by building energy performance simulation (BES) tools to estimate the annual heating and cooling demands of buildings. Most programs use constant values or correlations but do not consider in detail the 3D geometry or the dimensions and positions of the inlet and outlet openings. The correlations developed here take into account 3D geometry and the dimensions and positions of the inlet/outlet openings in opposite walls. A new methodology has been developed for the calculation of CHTCs in 3D enclosures from two 2D planes, one with a horizontal orientation and another with a vertical orientation. Comparing the results from correlations with those from computational fluid dynamics (CFD) simulations, the errors found are less than 15%. These correlations are easy to use in BES.

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.

Adaptation and validation of an existing bottom-up model for simulating temporal and inter-dwelling variations of residential appliance and lighting demands

The design and analysis of community-scale energy systems and incentives is a non-trivial task. The challenge of such undertakings is the well-documented uncertainty of building occupant behaviours. This is especially true in the residential sector, where occupants are given more freedom of activity compared to work environments. Further complicating matters is the dearth of available measured data. Building performance simulation tools are one approach to community energy analysis, however such tools often lack realistic models for occupant-driven demands, such as appliance and lighting (AL) loads. For community-scale analysis, such AL models must also be able to capture the temporal and inter-dwelling variation to achieve realistic estimates of aggregate electrical demand. This work adapts the existing Centre for Renewable Energy Systems Technology (CREST) residential energy model to simulate Canadian residential AL demands. The focus of the analysis is to determine if the daily, seasonal, and inter-dwelling variation of AL demands estimated by the CREST model is realistic. An in-sample validation is conducted on the model using 22 high-resolution measured AL demand profiles from dwellings located in Ottawa, Canada. The adapted CREST model is shown to broadly capture the variation of AL demand variations observed in the measured data, however seasonal variation in daily AL demand behaviour was found to be under-estimated by the model. The average and variance of daily load factors was found to be similar between measured and modelled. The model was found to under-predict the daily coincidence factors of aggregated demands, although the variance of coincident factors was shown to be similar between measured and modelled. A stochastic baseload input developed for this work was found to improve estimates of the magnitude and variation of both baseload and peak demands.

Numerical investigation of the energy performance of an Opaque Ventilated Façade system employing a smart modular heat recovery unit and a latent heat thermal energy system

The building sector is responsible for more than 40% of the EU’s total energy consumption. To reduce the energy consumption in buildings and to achieve the EU’s fossil fuel saving targets for 2020 and beyond 2050, the energy efficient retrofitting strategies are critically important and need to be implemented effectively. This paper presents a dynamic numerical investigation of the energy performance of an innovative façade integrate-able energy efficient ventilation system (E2VENT) that incorporates a smart modular heat recovery unit (SMHRU) and a latent heat thermal energy system (LHTES). A number of component simulation models, including SMHRU, LHTES, Cladding and Building Energy Management System (BEMS), were developed and then integrated using the TRNSYS software which is an advanced building energy performance simulation tool. On this basis, sizing, optimisation and characterisation of the system elements including the HVAC system and insulation layer thickness were carried out. The overall energy efficiency of the E2VENT system and its impact on the energy performance of a post-retrofit building were then investigated. In particular, the heating and cooling energy performance of the E2VENT façade module was numerically studied at five different climatic conditions in Europe. Furthermore, the innovative E2VENT retrofitting was compared with traditional retrofittings in terms of the energy efficiency and primary energy savings. It was found that the innovative E2VENT solution can achieve 16.5–23.5% building primary energy saving and compared to the traditional retrofitting, the E2VENT solution can achieve two times less primary energy consumption. Thanks to this efficiency the development of this solution for buildings retrofit is promising.

Ventilative cooling through automated window opening control systems to address thermal discomfort risk during the summer period: Framework, simulation and parametric analysis

Automated window opening control systems with integrated ventilative cooling strategies may significantly diminish the thermal discomfort and overheating risk of dwellings during cooling periods in temperate climates. One of the challenges with demonstrating the benefits of the systems is the lack of building performance simulation (BPS) tools which may represent precisely how actual algorithms are applied.The study supported herein aims to present a framework of how to simulate an advanced ventilative cooling algorithm of a window system on coupled BPS environments (ESP-r and BCVTB tools). Parametric analysis has been conducted to verify specific operational functions of the system. The analysis uses a renovated single-family house in Denmark (monitored June–August, 2016).Parametric analysis was highlighted that the performance of the developed ventilative cooling strategy for these climatic conditions was not affected by the number of opening steps (3 or 5) for low and medium natural indoor ventilation cooling set points (22–24°C). For all the examined spaces, the static trigger set points perform better than the dynamic for all the evaluating metrics and criteria that were included in this study. Under the proposed framework, the simulation of any other developed ventilative cooling concept or system is possible.

Analyses of the Control System Strategies and Methodology for Part Power Control of the Simple and Intercooled Recuperated Brayton Helium Gas Turbine Cycles for Generation IV Nuclear Power Plants

An important requirement for Generation IV Nuclear Power Plant (NPP) design is the control system, which enables part power operability. The choices of control system methods must ensure variation of load without severe drawbacks on cycle performance. The objective of this study is to assess the control of the NPP under part power operations. The cycles of interest are the simple cycle recuperated (SCR) and the intercooled cycle recuperated (ICR). Control strategies are proposed for NPPs but the focus is on the strategies that result in part power operation using the inventory control method. First, results explaining the performance and load limiting factors of the inventory control method are documented; subsequently, the transient part power performances are also documented. The load versus efficiency curves were also derived from varying the load to understand the efficiency penalties. This is carried out using a modeling and performance simulation tool designed for this study. Results show that the ICR takes ∼102% longer than the SCR to reduce the load to 50% in design point (DP) performance conditions for similar valve flows, which correlates with the volumetric increase for the ICR inventory tank. The efficiency penalties are comparable for both cycles at 50% part power, whereby a 22% drop in cycle efficiency was observed and indicates limiting time at very low part power. The analyses intend to aid the development of cycles for Generation IV NPPs specifically gas cooled fast reactors (GFRs) and very high-temperature reactors (VHTRs), where helium is the coolant.