Building Energy Simulation Research Articles

Evaluating the impact of high-resolution irradiation data on the daylight performance assessment of adaptive solar shading systems

Adaptive solar shading systems offer a dynamic solution to balance daylight availability and glare control in buildings. While building performance simulation methods are useful to understand the potential of these systems to improve daylight conditions in indoor spaces, it is unclear if established practices such as the use of hourly averaged climate data are sufficient to accurately estimate the benefits of adaptive solar shading in real-world conditions. This study investigates how the time resolution of irradiance data in simulation models affects the predicted performance of adaptive solar shading systems. To that end, a series of simulations for models with time resolutions from 1 to 60 min are run, and the results are compared to quantify the variation in daylight metrics between different resolutions. The outcome of this comparison study reveals that while hourly averaged irradiance data generally suffices for daylight performance estimations, it is also likely to overlook short-term periods of glare discomfort that would be present in high-resolution irradiance data. In the context of adaptive shading design, capturing these discomfort periods can be key to providing solutions that achieve specific visual comfort goals. This suggests that the need for high-resolution irradiance data is project-specific, which emphasizes the need for more tailored approaches in the representation of adaptive shading systems in early building design.

Impact of meso-scale weather uncertainty on building performance simulation outcomes

Building performance simulation is a complex experiment that has many inputs that are assumed tobe deterministic but are, in-fact probabilistic. In case of low-risk facile simulations, deterministic inputs may be assumed for the simulation parameters as they do not warrant experiments that cover all the possibilities. However, for high-risk design decisions, uncertainty assessment is a must to avert any failures that result in subpar operational performance.Weather in a city may be localized due to natural or altered urban attributes and any deviation from the statistically derived “average” weather data must be examined for uncertainties in performance outcomes. Case studies on three different buildings in Chicago and Atlanta indicate a large variance in performance outcomes when weather related uncertainties are accounted for in the Uncertainty Analysis as compared to deterministic weather from the nearest airport.The findings of this study suggest that for scenarios with large penalty on not meeting the performance targets, the modeler should be careful about the relative location of the proposed building in the city and should not, de-facto accept weather data that is derived from a remote location such as an airport.

Evaluation of Constructive Parameters to Minimize the Effects of Solar Radiation in Households with Tropical Climate

In tropical climates, the energy consumption of buildings is strongly influenced by solar radiation. However, more research needs to be done on how specific design parameters can mitigate the effects of solar radiation in tropical homes. This study uses simulations in EnergyPlus to evaluate how orientation, roof solar absorptance, overhang depth, glass type, and wall type affect energy consumption in a typical middle-income home in a tropical climate. The results highlight the benefits of adequate solar absorbance and correct orientation. Incorporating insulation into exterior walls does not offer significant improvements in the energy performance of the building. Key words. Building energy simulation, Tropical climate, parametric analysis.

Impact of uncertainty in building operation patterns and electric energy demand on the design and techno-economic performance of solar photovoltaic systems in different climates

The optimal design of renewable energy systems for buildings requires accurately estimating building electric energy loads. While this energy demand highly depends on how occupants and facility managers use building systems, operation-related uncertainty is rarely accounted for in the renewable energy design workflow. This paper presents a comprehensive framework to quantify the impact of uncertainty in building operation patterns and electric demand on the techno-economic performance of grid-connected photovoltaic (PV) systems (with and without battery storage). A hybrid methodology is proposed, combining and comparing two approaches to studying building performance under uncertainty: (i) physics-based building performance simulations coupled with parametric variations of occupant behavioral profiles (e.g., austere vs. wasteful), and (ii) data-driven modeling using a large national building dataset. The approach is illustrated and validated on small, medium, and large-sized office buildings in different climate zones (hot dry, mixed marine, and cold humid). Results show that different building operational patterns could lead up to 60% increase in building electric demand, which translated to similar increases in PV energy capacity and the corresponding capital costs of the systems. The findings emphasize the urgent need to better account for uncertainty in building operational patterns in the design workflows of buildings with PV generation and energy storage capabilities.

Improving building natural ventilation simulation accuracy: calibration of standard k − ε closure coefficients using design of experiment

Abstract The objective of this study is to improve the accuracy of the standard k − ε (SKE) turbulence model for natural ventilation simulation, a critical component in the analysis and design of energy-efficient buildings. The methodology involves generating 17 design points for the four closure coefficients (C ɛ 1,C ɛ 2,C µ ,σ k ) for the SKE model using 2k full-factorial design of experiment (DOE) with a centre point. A sheltered single storey-building with windward and leeward openings is modelled and simulated using computational Fluid Dynamics (CFD) to obtain the Root Mean Square Error (RMSE) and Factor of two observation (FAC2) for each design point. Response models for RMSE and FAC2 are then generated, and optimization is employed to maximize FAC2, minimize RMSE, and the combination of both. The results are validated and compared with experimental and simulation data reported in existing literature. The findings indicate that the indoor velocity profiles with closure coefficients obtained by maximizing FAC2 are better aligned with the actual experimental results. Compared with the existing calibration method, the proposed calibration method can yield superior results with fewer sampling points, highlighting the efficiency of the proposed approach. The calibration framework developed in this study can be extended to other simulation scenarios, contributing to the robust and efficient modelling of natural ventilation systems in various contexts. This research presents a novel calibration method for the SKE model in natural ventilation simulation, providing a more accurate and efficient approach compared to existing methods. The results have important implications for researchers and practitioners working in the field of building energy simulation and sustainability.

Evaluating the applicability of a machine learning methodology to improve TMY weather file generation for different Canadian climate zones

Typical meteorological year (TMY) weather files are used in building energy simulations to represent typical weather conditions for a location. The conventional approach to generating TMY weather files uses a set of universal weighting factors determined based on expert judgment. These universal weighting factors can overlook the distinct weather patterns between different locations and their influence on energy performance. A previous study used machine learning to generate location-specific weighting factors with respect to building energy performance. The objectives of this paper are: (1) To assess the applicability of the previous study across varying Canadian climates; (2) To investigate the feasibility of using standardized climate zone-based weighting factors to reduce the computational time associated with extracting location-based weighting factors while still considering local climate conditions to facilitate wider adoption of the proposed methodology. The investigation is conducted for a medium office building for 18 locations across six Canadian climate zones and generates two weather files, TMYSTATION and TMYCZ, for each location. The TMYSTATION weather files are generated using location-based weighting factors for each location whereas the TMYCZ weather files are generated using climate zone-based weighting factors for each location. The coefficient of variation of the root mean square error (CVRMSE) and normalized mean bias error (NMBE) are used to compare the performance of the proposed weather files with conventional weather files using the energy demands obtained through building simulation. The TMYSTATION and TMYCZ weather files outperform the conventional weather files in predicting the long-term energy performance of buildings. Although the TMYSTATION files marginally outperform the TMYCZ weather files in representing the long-term weather data, the convenience of standardized climate zone-based weighting factors allows the approach to be widely deployed.

Assessing and comparing the environmental impact of smart residential buildings: A life cycle approach with uncertainty analysis

While there is an increasing integration of connected devices in buildings to reduce energy consumption, little evidence exists regarding the effective environmental impact of the smart devices installed. One goal of this study is to comparatively assess the impact of integrating connected devices on the environmental performances of existing buildings. Additionally, it aims to discuss the added value regarding environmental performance between a strategy of integrating connected devices and a refurbishment strategy. A non-refurbished connected building will thus be compared with a non-connected refurbished building. The approach's novelty lies in carrying out an uncertainty analysis, which addresses uncertainties in the environmental modelling of sensors, heating gains, and other influential building parameters, such as occupancy scenarios and building lifespan. The Life Cycle Assessment model is built on the results of a dynamic building energy simulation and integrates environmental data on connected devices from ecoinvent. Environmental indicators are calculated using ImpactWorld+. The methodology is applied to a multi-storey residential tower in France, characterised by a low insulation level. The alternatives compared include the original building without sensors, two connected alternatives (monitoring and management) that lead to a reduction of heating loads by 10 and 30% respectively, and a refurbished alternative. Results confidently show that using connected devices is always better than keeping the original building state. Yet, refurbishing the building is the preferable option across almost all indicators. Finally, the results point out that energy gains allowed by connected devices should be better quantified in further work through measurement campaigns.

Opportunities and Barriers of Calibrating Residential Building Performance Simulation Models Using Monitored and Survey-Based Occupant Behavioural Data: A Case Study in Northern Spain

The performance gap caused by occupant behaviour (OB) is one of the main challenges to the accuracy of building performance simulations (BPS) models. Calibration of BPS models has shown great improvements in tertiary and single residential buildings. Nevertheless, the calibration in collective residential buildings is still uncertain. This study aims to identify the opportunities and barriers to the calibration of collective residential building BPS models for the analysis of heating energy consumption. For this, the research calibrates a real case study of a social rental housing building located in northern Spain. The method involves the adjustment of input data based on OB clusters, developed by monitorization and survey data and the statistical comparison of the results of normative models, calibrated models and real data. The results show an average improvement of 67% in hourly indoor temperature and 16% in hourly heating energy consumption in calibrated models, but still with a considerable performance gap. The main barriers to a higher accuracy are the wide diversity and lack of uniformity of OB patterns, uncertainty of parameters, and use of auxiliary heating systems. However, deeper monitorization and survey campaigns with the use of OB clusters can be a promising opportunity.

Comparison of data-driven stochastic window operation models for residential buildings

In residential buildings, window opening is typically one of the most significant parameters that affects indoor air temperature and thus the energy required for heating and cooling. While several field measurement campaigns have been undertaken, and datasets have been used to calibrate data-driven stochastic models based on different techniques, a critical comparison between these window operation models, analysing their suitability for building energy simulation, is needed. This study compares seven different modelling approaches including Gaussian distribution function, logistic regression, Markov chain, Markov-logit hybrid, classification tree, artificial neural network and random forest, trained and tested using measurements of 21 living rooms and 10 bedrooms from residential buildings located in Australian subtropical and temperate climates. Two types of modelling approaches were tested: individual, where each window operation was modelled individually, and cohort, where a common model was developed considering all the windows in the dataset. The stochastic nature of window opening was introduced to the models by Monte Carlo methods. True Positive Rate (TPR) and True Negative Rate (TNR), quantified using the Area Under Curve (AUC) method, were used to benchmark the performance of the different models. The classification trees and random forest were identified as more accurate methods (>0.74 median AUC) for cohort modelling, while artificial neural network and Markov-logit hybrid methods were identified as more accurate methods (>0.7 median AUC) for individual window operation modelling in building simulation applications.

A simplified modeling method for studying the thermal performance of buildings with vertical greening systems

Vertical greening systems (VGS) are considered an effective strategy to enhance the insulation performance of the building envelope. Existing methods predicting the performance of this strategy usually require a combination of mathematical modeling and building performance simulation, hence difficult for practical use. To simplify this work, this study has proposed a prediction method requiring building simulation only, by considering VGS as a shading component in the simulation work. To compare its performance, a model using conventional methods has also been proposed. A further simplification by neglecting the impact of transpiration has been investigated as well. To justify the implementation of the new method, it has been used to optimize the design of VGS for buildings located in the hot summer and cold winter areas in China. The validation result confirmed the accuracies of both methods, with the simpler method giving an insignificant lower accuracy than the conventional method, which can be compensated by its simpler prediction procedure. The implementation justification showed that for buildings in the HSCW area, VGS is suggested to be installed on the west facade, with a leaf area index of 4. This can help to reduce the heat transfer of the wall by 56.6%.

Development of Prefabricated Additional Insulation Elements for the Renovation of High-Rise Apartment Buildings

Prefabricated additional insulation elements have demonstrated success in renovating up to 5-storey apartment buildings. However, the unique challenges posed by high-rise buildings necessitate a closer examination of installation and long-term performance. In this study, we developed an additional insulation element specifically tailored for renovating high-rise apartment buildings and analysed its hygrothermal performance. To test the performance of the insulation elements, we installed a prototype and measured its performance at critical points along the external envelope. We calibrated a calculation model and applied building performance simulation software to explore various combinations of prefabricated elements. Our goal was to compare the associated risks and the key hygrothermal properties of these different material combinations within the insulation element. Critical points between the insulation layer and the wind barrier, as well as between the surface of the existing concrete panel wall and the vapour control layer of the additionally insulated exterior wall were analysed. The study's results indicate that the thermal resistance and water vapour permeability of the wind barrier layer, and the presence of an appropriate vapour control layer primarily influence the performance and moisture dry-out of a structure. Additionally, the study results indicate that the weather component has a higher impact than in lower buildings as the wind-driven rain loads are considerably higher in the upper parts of the high-rise buildings. The initial moisture (w = 110 kg/m3 at the height of the 9th storey and w = 117 kg/m3 at the height of the 16th storey) dry-out can last more than 3 years depending on the building type and materials used. This study underscores the importance of tailored solutions and vigilant moisture safety management in high-rise apartment building renovations, particularly when utilizing prefabricated additional insulation elements.

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in building performance simulation software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. The findings indicate that by analyzing measured data and adjusting the configurable parameters, the energy consumption of buildings can be significantly reduced. The close monitoring of indoor parameters by building operators and making corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, this paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Evaluating the sensitivity and robustness of occupancy models for building energy simulation during design

Occupancy modeling plays a crucial role in building energy simulation in building design, yet studies investigating the influence of model types and their input assumptions remain sparse. This study evaluates the sensitivity of parameters and model robustness to uncertainties in parameter ranges and data availability during the design phase. We assessed three occupancy models—deterministic, probabilistic, and agent-based—using an open dataset with occupancy data across four space types. Our findings reveal that the predictive performance of deterministic and probabilistic models is influenced heavily by a few of the most sensitive parameters. In contrast, more parameters affected the prediction performance of the agent-based model. This suggests a need to prioritize sensitive parameter estimation to enhance prediction accuracy in deterministic and probabilistic models. Specifically, the occupancy density and start/end time, followed by schedule fractions, emerged as highly sensitive due to their significant impact on average occupancy profiles. We also found that for hourly energy predictions, narrowing the range of the top five sensitive parameters decreases the coefficient of variation of the root mean square error (CV(RMSE)) by 3% and reduces its standard deviation by half. These results underscore the importance of careful parameter estimation to improve the reliability of occupancy models in building energy simulations.

Effectiveness and sustainability of solutions sets aimed at plus energy buildings. A multi-case and multi-domain investigation

PurposePlus Energy Buildings (PEBs) are gaining attention in construction for providing advantages not only on the singular building but also on higher levels, namely neighbourhoods and national grids. Different from the current standard of net zero energy buildings, PEBs lack broader and holistic investigations that consider energy, environmental, and economic performance indicators. These are needed to evaluate their often-debated effectiveness and environmental sustainability. To this purpose, this work assesses technical equipment functional systems aimed at PEBs by considering energy, environmental and cost performance indicators and by carrying out a multi-case and multi-domain investigation. MethodA parametric modelling is carried out based on building energy simulations and user energy profile modelling in 16 case studies. Relevant Key Performance Indicators are derived and followed to attempt result clustering and to derive general considerations for the designed solution. FindingThe study showed that the effectiveness of such systems is limited if PV modules are located on the roof exclusively. Moreover, heat pumps and PV technologies need to be better coupled and harmonised in subarctic regions. Overall, centralised systems perform better, and environmental and economic advantages depend on the national energy and economic context. Such results can be considered valid under the same conditions and circumstances; therefore, an extension of case studies is needed.

Unlocking energy and economic benefits of integrated green envelopes in office building retrofits

Green envelopes show significant potential in energy conservation and thermal comfort enhancement in office buildings. This study presents a comprehensive method integrating field tests, building energy simulations, and optimization in office buildings in Chongqing, China. A 20-year life-cycle cost analysis was conducted to evaluate the benefits of green roofs (using sedum lineare container-type planting modules) and green façades (featuring vertical kudzu greenery systems). The results indicate that green roofs could reduce peak temperatures by up to 34.3 °C, leading to daily energy savings of 12.5 % during summer. Green façades decreased the wall exterior surface temperatures by up to 3.4 °C and 5.1 °C on the test cold and hot days, respectively. The maximum annual building electricity savings per green-modified area was 22.8 kWh/m2 for the poorly-insulated building and 5.4 kWh/m2 for the well-insulated one. This study highlights the cost-effectiveness of green envelopes in improving building efficiency. The optimal retrofit measure for the poorly-insulated building was the combination of green roofs and daylighting dimming control systems, yielding a 20-year life-cycle net savings per floor area of 81.5 CNY/m2. For the well-insulated building, the daylighting dimming control system alone provided optimal net savings per floor area of 26.4 CNY/m2.

A systematic approach to manual calibration and validation of building energy simulation

PurposeThis study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.Design/methodology/approachA standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO2 levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.FindingsDiscrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO2 levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.Originality/valueThis paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.

Agile, continuous building energy modeling and simulation

Digital twins have emerged as highly valuable tools for model-based planning, simulation and optimization over the last couple of years, thereby demonstrating considerable potential for application within the construction industry. The introduction of building information modeling (BIM) has effectively established a standardized approach to representing building models. However, in practice, many of these models currently exhibit limitations as to their quality, specifically concerning the level of detail they encompass. In addition, BIM models too often are locked inside a specific vendor’s tool which readily implies a lack of platform independence, or interoperability, which, however, is essential for facilitating single and regressive, i.e., after a design change, model-based building performance simulations. Model-based engineering has effectively addressed comparable challenges within the domain of software engineering over the past decades by facilitating the integration and interoperability of models from various origins, by capitalizing on model-based tool integration. Prompted by these advantages, this study introduces a model-based tool environment that addresses the aforesaid challenges concerning BIM model quality and interoperability. Taking advantage of our proposed model-based tool environment, we implement an agile, continuous planning process for regressive, model-based building performance simulations, thereby enhancing building energy efficiency planning.

An explainable & prescriptive solution for space-based energy consumption optimization using BIM data & genetic algorithm

Creating energy-efficient buildings is a multifaceted challenge that involves carefully considering architectural, mechanical, and electrical parameters. With the increasing emphasis on low-energy building mandates and sustainable construction practices, integrating energy performance simulation into the design process has become imperative. However, existing methods often struggle to handle the vast amount of design information and to explore optimal alternatives effectively. While recent approaches such as Building Information Models (BIM) and data-driven solutions show promise for energy analysis, they frequently lack transparency and robust design optimization capabilities, limiting their applicability in critical contexts within the Architecture, Engineering, Construction, and Operation (AECO) industry. In response to these challenges, this research proposes a novel prescriptive model aimed at addressing these limitations. The proposed methodology integrates building energy simulation with optimization techniques, utilizing BIM data and a Genetic Algorithm (GA). The Genetic algorithm provides some optimized solutions, including different alternatives of geometry, material, electrical, and mechanical elements to help engineering for data-driven decision-making. This integration aims to develop a model that is both prescriptive and explainable for indoor building design. Leveraging the value engineering method, the proposed approach seeks to strike a balance between energy consumption, functionality, and cost. By doing so, it aims to enhance energy efficiency while providing tangible design optimization solutions.

Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model

The urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

Using automated design appraisal to model building-specific devaluation risk due to land-use change

Uncertainty of spillover effects – including property devaluation - from proposed land-use change elicits opposition to local development. This hinders cities’ ability to implement land-use policy aimed at housing affordability and environmental sustainability. The uncertainty arises from the limitation of current methods to quantify both the physical exposure and financial sensitivity to changes in urban environmental characteristics and amenities. Hence, this paper introduces an integrated approach to quantify the direct and indirect effects of a proposed urban development, using parametric modeling, building performance simulation and automated design appraisal (ADA). ADA, a novel aspect of this study, predicts the market price of a generated building design concept within a local urban context. To demonstrate its practical utility, this paper examines property value at risk due to nearby development associated with changes to visual landscape quality. The results shed light on the importance and spatial variability of the price-amenity gradient, identifying properties that are financially at risk to the visual impacts of local development. Beyond building-specific risk modeling for the development planning stage, this work serves as a blueprint for architectural design optimization procedures, in which economic performance is evaluated based on learned preferences derived from financial market data.