Building Energy Simulation Model Research Articles

Sustainable metal-organic framework based paper sheet as wallpaper for passive indoor moisture control

Passive indoor moisture control is a meaningful way to reduce HVAC operation and improve building energy efficiency. Metal-organic frameworks (MOFs) have been considered one of the most promising sorbent materials for passive indoor moisture control. However, the powder form of MOF is challenging to use in the built environment. Typical shaping methods like pellets, beads, and coating can lead to problems like partial pore blocking, insufficient mechanical stability, amorphization, and low material utilization. Here, we report a new sustainable MOF paper sheet comprising 25% cellulosic fibers and 75% MOF MIL-100(Fe) powder. The porous polymer matrix provides good support and fixation for MOF particles, so it’s more applicable in the built environment. The crystalline and pore structure characteristics of the MOF in the membrane are well preserved. Hydro characteristic test results show that MIL-100(Fe) paper sheet has high water vapor uptake, S-shape isotherm, mild regeneration condition, and high MBV. Then, a numerical model was developed to calculate the relation between a material’s MBV and its thickness. MIL-100(Fe) paper sheet with the most efficient thickness was applied as wallpaper in a building energy simulation model. The simulation result shows that the MIL-100(Fe) paper sheet is promising for passive indoor moisture control. It can remove about 45%-55% of the latent cooling load in most European climate types.

Application of Bayesian models in building information modeling of residential buildings

This paper lays forth a process for continuing Bayesian calibration of full-scale building energy simulation (BES) models by making use of data obtained from building information modeling (BIM) and building energy management systems (BEMS). According to a survey taken in China, it is possible that importing data from BIM and BEMS will dramatically cut down on the amount of time and effort required for the continuing calibration of BES models. After that, the continuous calibration approach that had been developed was examined with the use of a case study that was based on the actual calibration of a building. In China, Building Information Modeling (BIM) and data on the amount of electricity used each month over the course of the preceding three years were both incorporated into the case study. According to the findings, a non-continuous technique on the test dataset fared worse than a continuous Bayesian calibration method in terms of prediction accuracy and reduced uncertainty. The normalized mean biased error (NMBE) and the coefficient of variation of the root mean square error (CVRMSE) are both discussed in this research, and comparisons are drawn between the two.

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

Applicability evaluation of a temperature humidity index-controlled ventilation system in livestock using a building energy simulation model

In general, for livestock building, a simple system for controlling the HVAC system that is based only on the indoor air temperature is applied. This study evaluated the applicability of temperature humidity index (THI)-based ventilation systems installed in livestock buildings using a building energy simulation model. Based on the monitoring data of an actual pig house, the integrated predictive performance of the indoor temperature and humidity of the building energy simulation model was calibrated. Additionally, the indoor environment of the existing temperature-based control and THI-controlled ventilation systems was compared through simulations. The average THI and the section exceeding the THI threshold were both lower compared to the existing system. This was because the part load ratio of the fans of the THI-controlled ventilation system was higher than that of the existing system, indicating better ventilation. These results suggest that THI-based ventilation systems can overcome the limitations of existing temperature-based systems and effectively improve the thermal stress management of livestock buildings. This study portrays that the indoor environment can be improved by simply changing control variables, while presenting the field applicability and research value of THI-based control systems in the management of livestock environments.

Preparation of high-strength waste-derived eco-friendly ceramic foam as face brick and its estimation of building energy consumption for thermal insulation

Waste-derived ceramic foams as a logical solution for the reutilization of industrial solid wastes have been prepared using red mud and fly ash, eggshell as a foaming agent, and di-sodium tetraborate as a glass former. The effect of foaming (1–5 wt %) and glass-forming (20 wt %) agents on the microstructure, physico-mechanical properties, and chemical stability of foams are investigated. The optimized product (RFE3) with a total porosity (33%) and bulk density (2.14 g/cc) reveals high compressive strength (109.75 MPa) and low thermal conductivity (0.93 W/m•K) that is suitable as a load bearing outside building block face brick. The theoretical building energy simulation model using heat balance (HBM) and cooling load temperature difference (CLTD) method has revealed that the average annual energy conservation in India is 21816 MJ. Compared with the traditional face brick, our developed high strength ceramic foam face brick with low thermal conductivity saves satisfactory building energy consumption. The present work is relevant to international research regarding environment friendly building envelope construction.

Evaluating different levels of information on the calibration of building energy simulation models

Evaluating different levels of information on the calibration of building energy simulation models

Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review

The need to improve the sustainability of intensive livestock farming has led to an increasing adoption of Building Energy Simulation (BES) models for livestock houses. However, a consolidated body of knowledge specifically dedicated to these models is lacking in literature. This gap represents a significant obstacle to their widespread application and scalability in research and industry. The aim of this work is to pave the way for scaling the adoption of BES models for livestock houses by providing a comprehensive analysis of their application, development, and validation. For this aim, a systematic review of 42 papers—selected from over 795 results from the initial database query—is carried out. The findings underscored a growing body of research that involves BES models for different purposes. However, a common approach in both model development and validation is still lacking. This issue could hinder their scalability as a standard practice, especially in industry, also considering the limitations of BES models highlighted in this work. This review could represent a solid background for future research since provides an up-to-date framework on BES models for livestock houses and identifies future research opportunities. Moreover, it contributes to increasing the reliability of BES models for livestock houses by providing some recommendations for their validation.

Quantifying the impact of Covid-19 on the energy consumption in the low-income housing in Greater London

Covid-19 has caused great challenges to the energy sector, particularly in residential buildings with low-income households. This study investigates the impact of the confinement measures due to the Covid-19 outbreak on the energy demand of seven residential archetype buildings in Greater London. Three levels of confinement for occupant schedules are proposed and compared with the base case before Covid-19. The archetypes, their boundary conditions, and input parameters are set up according to statistics from English Housing Survey (EHS) sample data for low-income housing. The base case scenario (normal life without confinement measures) is validated against the measured data energy consumption from the National Energy Efficiency Data-Framework (NEED) statistics. The results show that electricity consumption is significantly lower than that for heating and hot water for all the archetypes. By comparing the base case scenario with the full Covid-19 lockdown scenario, the results indicate that heating and hot water consumption (kWh) for all the residential archetypes increases, on average, by 10%, and total electricity demand (kWh) increases by 13%. The study highlights the importance of introducing detailed occupancy profiles in multi-zone building energy simulation models during a pandemic that leads to a greater shift towards home working, which may increase the risk of fuel poverty in low-income housing.

Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season

This paper presents a critical advancement in Building Energy Modeling (BEM) through an empirical validation approach using a high-quality dataset from a multizone commercial office building in Oak Ridge, TN, USA. BEM is widely utilized in diverse construction applications, but its effectiveness relies on the accuracy of its predictions. The study focuses on empirical validation of input parameters in BEM, including building envelope data, infiltration modeling, and rooftop unit system performance curves. The validation of simulation input parameters leads to substantial improvements in the accuracy of simulation results. Notable both NMBE and cv (RMSE) values are reduced by 0.5 % for indoor air temperature and 17 % for indoor air relative humidity compared to the previous model. At the system level, both NMBE and cv (RMSE) values are reduced by 2 % for fan energy consumption and 4 % for cooling energy consumption, compared to the previous model. A literature review highlights a significant gap in empirical validation studies, which predominantly concentrate on either component-level or whole building validation. Furthermore, many studies employ simplified setups that may not faithfully represent the complexities of multizone commercial buildings. This paper distinguishes itself by emphasizing the critical importance of component-level input parameter validation. It underlines the need to validate data related to building envelope components and HVAC system performance curves, resulting in more accurate simulation outcomes. The utilization of actual multizone commercial building data enhances the study's practical relevance. In summary, this research underscores the pivotal role of input parameter validation in enhancing the accuracy and reliability of BEM.

Modelling analysis and performance evaluation of a novel hybrid CdTe-PCM PV glass module for building envelope application

In this work, a novel hybrid CdTe-PCM PV glass module (CdTe-PCMG) was proposed, fabricated and tested. A mathematical optical-electrical-thermal coupled model was established and validated against the experiment data under different ambient conditions. Based on the validated model, a building energy simulation (BES) model integrated with the novel modules as building windows was established. Comparison case study was respectively conducted between the building model with single PV glass module under a continuous hot/cool week. Impacts of several parameters including the PV coverage, ambient conditions, PCM thickness and phase change range on the comprehensive electrical and thermal performance was analyzed. Annual performance in five different climatic cities was predicted to discuss the PCMG's regional application potential. Main conclusions are: (1) The utilization of PCM has improved the electrical output of the PV glass and enhanced the indoor thermal comfort when applied on the building envelope; (2) According to the parameter analysis under the cool week, the optimal PCM layer thickness and phase change range were 3.5 cm and 22 °C ∼ 24 °C; (3) Based on the annual performance predictions, the PCMG was more suitable to be applied in the regions with abundant solar radiation resource or mild climate condition.

SHP2SIM: a python pipeline for Modelica based district and urban scale energy simulations

ABSTRACT Energy simulation models are crucial to estimate the energy demand of buildings, especially for prospective planning on a district or city scale. As required input data is not available in many cases, an automated model generation workflow is needed. Existing workflows have several disadvantages, including: (i) dependence on large input datasets of existing buildings; (ii) no 3D representation to support the planning process; (iii) they are proprietary solutions. The pipeline ‘SHP2SIM’ is an open-source python pipeline enabling enrichment and generation of building energy simulation models based on little input data for district and urban scale. The pipeline is tested by simulating the heat load for a district with 27 buildings and validated for one building: R squared is 0.9825, CV(RMSE) is 22.10%, and NMBE is 4.06% on a monthly basis. To enable reproducibility and encourage open science, input data, output models, and the pipeline are openly available (https://github.com/tug-cps/shp2sim).

Automated Generation of Energy Profiles for Urban Simulations

Urban simulations play an important role on the way to a climate neutral society. To enable early assessment of different energy concepts for urban developments, energy profiles for different building types are needed. This work describes the development and use of a new engineering tool GenSim to quickly and reliably generate energy profiles for urban simulations and early building energy predictions. While GenSim is a standalone tool to create energy profiles for early design assessment, it was developed in the context of urban simulations to primarily support energy efficient urban developments within Germany. Energy engineers quickly embraced the tool due to its simplicity and comprehensible results. The development of the tool was recently switched to open source to enable its usage to a broader audience. In order to foster its development and use, a detailed testing framework has been established to ensure the quality of the results of the tool. The paper includes a detailed validation section to demonstrate the validity of the results compared to a detailed building energy simulation model and actual measured performance data.

Building energy demand of urban blocks in Xi'an, China: Impacts of high-rises and vertical meteorological pattern

High-rise buildings have been promoted in high-density cities. The impact degree of urban high-rises on the building energy demand at the urban block scale is determined by many factors, including the effects of the vertical meteorological pattern (VMP), which have been discussed insufficiently. In this study, the VMP of urban areas in Xi'an, China was observed, and a building energy simulation model that revealed the “urban block form–outdoor climate–building energy” nexus was developed with the URBANopt plugin on the Grasshopper platform. The impacts of the VMP on the building heating and cooling energy demands of different urban blocks were then predicted. In January, the VMP led to building heating load increases of 0.7–1.6 kWh/m2 (16–52%) and 0.0–1.1 kWh/m2 (0–35%) in the residential and mixed-use blocks, respectively. In July, the ranges of the building cooling load changes produced by the effects of the VMP were 0.0–0.1 kWh/m2 (0–1%) and −0.3–0.2 kWh/m2 (−2%) in the residential and mixed-use blocks, respectively. Furthermore, the VMP affected the relationship between the urban block form and the building energy demand. Based on the relationship, we provided form-optimization advice for different functional urban blocks. The urban high-rise mechanisms in high-density cities and their impacts on the building energy demand were discovered in this study. This can help decision-makers to optimize urban planning while promoting urban sustainability.

In-situ empirical validation of common indoor climate parameters in an inhabited multizone dwelling

Combined building indoor climate and energy simulation models only recently gained vast popularity and their application has been moving from the research community to a broader audience. Yet, in-situ empirical validation of this new generation of complex multi-purpose dynamic simulation models has lagged behind. Using a dynamic multizone building indoor climate and energy simulation model in Modelica with the IDEAS library and buoyancy driven airflow components (validated with CONTAM), this research presents model validation results and lessons learned from an in-situ empirical validation study of common indoor climate parameters (i.e., indoor air temperature (), relative humidity (RH) and CO2 concentration (CO2)) for an inhabited and mechanically ventilated case study dwelling in The Netherlands. The simulation results show that the latest generation of building indoor climate and energy models in Modelica have great ability to accurately predict common indoor climate parameters in multizone inhabited dwellings (provided that user behavior info is available). Evaluation metrics for the three studied parameters show excellent calibration criteria (i.e., MAE between 0.60–0.78 °C (), 3.5–4.6% (RH) and 88–181 ppm (CO2)) and the accompanying graphs corroborate the findings. In the event that no motion sensor data is available, statistically generated occupancy profiles prove good representative alternatives on the condition that basic info is available about the number of inhabitants and the inhabitants’ lifestyle. In-situ monitoring for empirical model validation proves to be a real challenge full of (un)foreseen obstacles.

Development of an optimization model for decision-making in building retrofit projects using RETROSIM

ABSTRACT Nearly-zero energy buildings are standard for new constructions in the European Union. The challenge for decarbonized cities is retrofitting the existing buildings. Retrofitting the existing buildings provides considerable opportunities for improving occupants’ comfort and reducing global energy consumption and greenhouse gas emissions. Building retrofit is being considered one of the main approaches to achieve sustainability in the built environment at relatively low-cost and high-uptake rates. Although a broad range of retrofit technologies are readily available, methods to ascertain the most suitable set of retrofit actions for specific projects are still a practical challenge. This article first aims to develop and present a simulation-based multi-objective optimization model, named RETROSIM, to quantitatively assess technology choices in a building retrofit project. RETROSIM is a combination of a building energy simulation model and RETROSIM proprietary optimization engine. Secondly, in this study, RETROSIM is used to evaluate a building retrofit project as a case study to show the functionality of the proposed method. The study initiates with the single optimization of objective functions focusing on the building’s characteristics and performance: primary energy consumption, global costs and thermal discomfort hours. Then this methodology is used to study the interaction between these conflicting objectives and evaluate their trade-offs.

Assessing urban micro-climates with vertical and horizontal building morphological cutting deep transfer learning neural networks

The weather condition is the major external factor that affects annual building energy usage and determines the reliability of building energy assessment and simulation models. In city-scale studies, buildings’ energy assessment and simulation rely on one universal Typical Meteorological Year weather condition for the whole city. However, due to the unique morphological features and environmental conditions, each location in a city has its unique microclimate conditions. To predict and simulate more accurate and realistic building energy usage, this study developed a deep transfer learning neural network that can integrate morphological features and frontal areas' depth distribution. The frontal areas are represented as stripes with different cutting methods to reflect the impact of depth distribution. To validate the proposed method, this study conducted an on-campus experiment with a wireless sensing system. The results suggest that the proposed stripe-enabled geometry segmentation method can effectively improve the accuracy of local microclimate condition prediction. Also, the combined vertical and horizontal cutting method shows the highest performance.

Building Energy Simulation Model Application to Greenhouse Microclimate, Covering Material and Thermal Blanket Modelling: A Review

This review documents the present knowledge and knowledge gap in applying building energy simulation (BES) dynamic models to greenhouses. The focus of this review is to compile the literature on the BES dynamic model of greenhouse microclimate, covering materials, energy requirements and thermal blankets using the Transient System Simulation version 18 (TRNSYS 18) software. Fifty-two journal articles, mostly Science Citation Index (SCI) and Scopus index journals, on BES development and simulation of greenhouse microclimate, greenhouse energy requirement, covering materials and thermal blankets were reviewed. These researchers sought to optimise greenhouse crop production. The main features of the TRNSYS 18 software for BES development are outlined; each research consulted for this review successfully developed, simulated and validated its BES. However, none of these developed models included the vapour pressure deficit (VPD) as a greenhouse microclimate factor, an essential climate parameter. In conclusion, this study demonstrates that applying a BES developed using TRNSYS has excellent potential to optimise greenhouse crop production and help adapt appropriate climate control strategies and energy-saving techniques. However, it is recommended to include VPD in future BES model development.

Outdoor thermal comfort in urban neighbourhoods by coupling of building energy simulation and computational fluid dynamics

When evaluating the outdoor environment, it is essential to improve the accuracy of outdoor thermal comfort (OTC) modelling by investigating the simultaneous interactions of both convective and radiative fluxes. The majority of the existing models, however, employed to evaluate thermal comfort, do not consider these co-effects.This study aims to develop a novel and comprehensive framework for OTC modelling while using non-isothermal airflow and surface temperatures within street canyons. For this purpose, a dynamically coupled building energy simulation (BES) and computational fluid dynamics (CFD) model, previously developed by authors, is used to provide detailed analysis of convective fluxes. These values in addition to the simulated radiative heat fluxes are then utilized to calculate the OTC at a neighbourhood case study during a typical hot day.The results show substantial changes (6.5% higher) in the OTC results using the newly developed coupled model in comparison to traditional (standalone) approaches. For example, in the coupled approach, the OTC values experience a wider range which peaks to at noon . Moreover, physiological equivalent temperature (PET) values are higher which shows higher level of discomfort and heat stress range compared to the standalone models.

Effect of envelope characteristics on the accuracy of discretized greenhouse model in TRNSYS

TRNSYS is a common tool that has been recently used to model and simulate greenhouse energy demand and utilization using building energy simulation (BES). Previously, a single thermal point was used for validation, ignoring the distribution of greenhouse climate parameters, especially the temperature. Temperature variation often leads to thermal stratification, prompting researchers to propose volume discretization in dynamic greenhouse simulations. In this context, the effect of envelope characterization on the accuracy of discretized TRNSYS BES model was developed to determine the best BES model under a free-floating regime. The combination of the number of layers [double (D) and single (S)], geometry mode [3D and manual (M)], and layer type [massless (M) and no glazing window (W)], led to the development of five models: D_3D_M, D_3D_W, D_M_M, S_3D_W, and S_M_M. The simulation was performed in a standard radiation mode, and the output parameters were temperature and relative humidity (RH). R2 and the root square mean error (RSME) were used to check the fitness and degree of deviation, respectively, to validate the models. Analysis of variance (ANOVA) was employed to investigate the significant differences among the models, whereas contour plots were used to compare the distribution pattern between the significant models and experimental data. Validation of the models showed that the obtained R2 values ranged from 0.86 to 0.95, and the RSME values for the temperature were between 2.64 °C and 3.91 °C. These values were 0.91–0.93 and 19.72%–30.32% for RH. The ANOVA (p < 0.05) result exhibited significant differences between the S-scenario models and experimental central points in temperature and RH. The D- and S-layer scenarios with a 3D geometry and massless layer showed similar distribution with their corresponding experimental greenhouses. Hence, 3D_M was regarded as the best combination in the discretized BES model.