Building Energy Simulation Model Research Articles

Challenging Issues in Stochastic Calibration based on Bayesian paradigm for Building Energy Model

Bayesian calibration has been used to transform the prior distributions of unknown inputs inherited in a building energy simulation model into the trustworthy posterior distributions. It obtains the posterior distributions using a joint distribution composed of a likelihood function and prior distributions of unknown inputs in the Bayesian paradigm. In other words, it provides higher benefits in terms of a stochastic approach than the deterministic calibration and the feasibility of their calibrated results was sufficiently discussed. However, challenging issues in Bayesian calibration still remains as follows: (1) inappropriate selection of prior distributions, (2) truncated sample dataset of the likelihood functions. The aforementioned issues can increase the risks of Bayesian calibration. This paper aims to inform the risks of Bayesian calibration associated with the aforementioned issues through a reference case study. For this study, the Gaussian Process (GP) emulator, which can be regarded as a meta-model of Building Performance Simulation (BPS) tools, was used to reduce the simulation run-time. Bayesian calibration using the GP emulator was implemented with what-if scenarios considering the aforementioned issues. And then the validated models were used for a stochastic retrofit analysis of glazing systems. With the results of the estimated posterior distributions, validation, and stochastic retrofit, this paper presents Bayesian calibration issues regarding the selection of prior distributions and sample dataset of the likelihood functions.

Building model calibration using energy and environmental data

A large number of randomly interacting variables combine to dictate the energy performance of a building. Building energy simulation models attempt to capture these perturbations as accurately as possible. The prediction accuracy of building energy models can now be better examined given the widespread availability of environmental and energy monitoring equipment and reduced data storage costs. In this paper a set of two calibrated environmental sensors together with a weather station are deployed in a 5-storey office building to examine the accuracy of an EnergyPlus virtual building model. Using American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guide 14 indices the model was calibrated to achieve Mean Bias Error (MBE) values within ±5% and Cumulative Variation of Root Mean Square Error (CV(RMSE)) values below 10%. The calibrated EnergyPlus model was able to predict annual hourly space air temperatures with an accuracy of ±1.5°C for 99.5% and an accuracy of ±1°C for 93.2% of the time.

An indirect validation of convective heat transfer coefficients (CHTCs) for external building surfaces in an actual urban environment

The convective heat transfer is an important component in the estimation of thermal balance of energy for arrays of buildings immersed in a turbulent boundary layer. This study proposes a novel cost-effective validation approach using targeted field measurements and numerical simulations as an alternative to the wind-tunnel and full-scale field measurement approaches that typically require significant human resources and instrumentation to develop convective heat transfer coefficient (CHTC) for buildings located in the urban environment. This study first introduces new CHTC correlations for regular arrays of cubic buildings. A field measurement is then conducted in an actual urban thermal environment characterized by the plan area density λp = 0.25. Afterwards, this urban thermal environment is numerically simulated using CHTC values for the specified λp and appropriate thermal boundary conditions. The results of the numerical simulations are compared with the measured air temperatures to indirectly validate the CHTCs for external surfaces of buildings. The results show that the difference between the simulated and measured air temperatures is small and typically within 5%. Furthermore, this study created a calibrated building energy simulation model to deploy newly developed CHTCs for building heating energy consumption calculations. Specifically, the energy simulations used both newly developed and commonly used CHTCs to analyze the influence of CHTCs on the heating energy simulation results. A comparison between the simulated heating and actual heating energy consumption shows that the developed CHTCs have a positive influence on the accuracy of the energy simulation results.

Evaluation of a multi-stage guided search approach for the calibration of building energy simulation models

This paper is focused on increasing the knowledge on methods for calibrating BES models and to get more insights of different approaches for the optimization of the calibration process. The paper will be centred in the evaluation of a multistage guided search approach. It defines an iterative optimization procedure which starts with the assignment of probabilistic density functions to the unknown parameters, followed by a random sampling and running batch of simulations. It then finishes with an iterative uncertainty and sensitivity analysis combined with a re-assignment of the ranges of variation of the strong parameters. The procedure converges when no new influencing parameters are found. This method is applied to a real case study consisting of an unoccupied office building located in Lleida (Spain). The measured indoor temperature has been used to determine the uncertainty and precision of the method. The effect of the size of the sampling, the number of iterations and the parameters of the global sensitivity method are analyzed in detail. The results of this paper exemplify the degree of accuracy of multistage guided search approaches, and illustrate the reasons how these analyses can contribute to the improvement of more refined calibration methods.

Robustness of a regression approach, aimed for calibration of whole building energy simulation tools

An approach, able to easily and effectively integrate field measured data in whole building energy simulation (BES) models is crucial to increase simulation accuracy for existing buildings. In this paper, the robustness of a linear regression method for extracting transmission losses above ground (including air leakage) and ground heat loss parameters are analyzed. The regression method is evaluated on two documented and monitored multifamily buildings with mechanical supply and exhaust ventilation systems, with and without heat recovery.The obtained results are found to be robust, with variations less than 2% in the extracted estimates of transmission losses above ground (including air leakage) and with a high goodness of fit (R2>0.96) against measured data from two years. In addition, the estimations of the buildings ground heat loss were in good agreement with calculations in accordance with EN ISO 13370:2007. The high quality output from the used regression method serves as good prerequisites for the method to be used in conjunction with BES models to aid the analyst in a BES calibration process.

Development of an energy prediction tool for commercial buildings using case-based reasoning

Building energy prediction is a key factor to assess the energy performance of commercial buildings, identify operation issues and propose better operating strategies based on the forecast information. Different models have been used to forecast energy demand in buildings, including whole building energy simulation, regression analysis, and black-box models (e.g., artificial neural networks). This paper presents a different approach to predict the energy demand of commercial buildings using case-based reasoning (CBR). The proposed approach is evaluated using monitored data in a real office building located in Varennes, Québec. The energy demand is predicted at every hour for the following 3h using weather forecasts. The results show that during occupancy, 7:00–18:00, the coefficient of variance of the root-mean-square-error (CV-RMSE) is below 13.2%, the normalized mean bias error (NMBE) is below 5.8% and the root-mean-square-error (RMSE) is below 14kW. When the statistical criteria are calculated for all hours of the day, the CV-RMSE is 12.1%, the NMBE is 1.0% and the RMSE is 11kW. The case study demonstrates that CBR can be used for energy demand prediction and could be implemented in building operation systems.

A review of methods to match building energy simulation models to measured data

A review of methods to match building energy simulation models to measured data

Estimation of Potential Transportation and Building Energy Performance of Commercial Office Site Alternatives

The energy efficiency of urban infrastructure is in large part influenced by the efficient utilization of transportation and building systems. Opportunities for more efficient utilization of transportation and building systems are available in the context of commercial office building/site selection. Office location decision makers have an opportunity to select buildings and locations that potentially minimize building and transportation energy consumption within a given urban context. The objective of the research presented in this paper was to apply a calculation framework for estimating the potential transportation energy and building energy consumption of commercial office building/site alternatives. The calculation framework was applied to case studies of commercial office buildings/sites that represent typical developments found in the transportation and land-use context of the Atlanta, Georgia, metropolitan region. The framework leverages building energy simulation models and regional travel demand model data to estimate energy performance under uncertainty. Importantly, the calculation results indicate that transportation is a major determinant of commercial office building/site energy performance. Incorporation of the framework into sustainable development policy or market transformation tools (e.g., green building rating systems) could accommodate more performance-based planning of efficient infrastructure utilization.

Trial application of ASHRAE 1051-RP: calibration method for building energy simulation

ASHRAE research project 1051-RP generated a method to improve the process of calibrating whole building energy simulation models based on monthly utility data. The approach, using manual generation of simulation model variations, was applied to a 12,000 m2 high-performance, dual energy, cold climate building. This led to 27 models that met the ASHRAE Guideline 14 monthly goodness-of-fit criteria for electricity, but had fit values for gas that were about 5–7 times the normalized mean bias error (NMBE) acceptance threshold. Five models met the criteria for natural gas and had acceptable coefficient of variation of the root-mean-square error for electricity, but NMBE was about 100% too high. Use of finer interval monitored data yielded a model with electrical NMBE about 60% above the acceptance threshold, and gas use about 200% above. Hourly analysis of the thermal energy demand on the plant showed wide discrepancies with the estimates on an hourly and half daily basis.

Low temperature radiator heating distribution and emission efficiency in residential buildings

Low and nearly zero energy buildings with decreased heating need can utilize low temperature heating systems for energy efficient heating. Distribution and emission losses for low temperature radiator heating cannot be found from European standards or scientific literature. The use of the losses of conventional systems can result in significant overestimation of heating energy use. In this paper, distribution and emission losses of low temperature and conventional radiator heating system were determined in North and Central Europe climates for low energy detached houses and apartment buildings. Detailed dynamic components of heating system in the whole building energy simulation model allowed to quantify these losses. Main findings of the study show that distribution losses can be controlled with low temperature heating curves and emission losses with PI type thermostats. For conventional systems the losses higher than 50% of heating need were calculated in the apartment building. With low temperature heating curve, PI thermostats and limited heating period distribution and emission losses were possible to keep below 1% in detached houses in both climates. In apartment buildings the minimal achievable losses were significantly higher, between 6 and 12% in North and Central European climates, respectively. Proportional thermostats add 2 to 6% to these losses. Based on results, heating curve of 45/35°C can be recommended for detached houses and even 40/30°C for apartment buildings. Insulating distribution and connection pipes in heated spaces proved to have no practical effect on heat losses. Compared to EN 15316-2-1:2007, the losses were significantly lower especially for low temperature heating curves. A new set of tabulated values is proposed for the revision of the standard. It was possible to explain the mechanism of losses, but still it was not possible to calculate losses from hourly energy need data with the correlation equation leading to overestimation by factor of about 10 compared to dynamic simulation.

Integrating probabilistic methods for describing occupant presence with building energy simulation models

This paper presents a method for developing a probabilistic-based occupancy model that focuses on occupants’ long vacancy activities (greater than 1 week) and other potential building underutilization that is further integrated with a building energy simulation model. The combined model is then applied toward an existing Leadership in Energy and Environmental Design (LEED) certified military dormitory and later compared with corresponding values from the energy model's original prediction as well as actual building energy data. The occupancy model simulates annual building occupancy rates comprised of weekly values based on the frequency, duration, and seasonality of occupants’ long vacancy activities. The energy model uses the simulated occupancy rates to yield the building's predicted range of energy performance. Applying the combined model to the existing LEED building resulted in an improved, predicted Energy Use Intensity (EUI) mean value of 612MJ/m2 as compared to the original model and actual EUI values of 691 and 590, respectively. While the model also demonstrated its utility in describing the change in predicted performance over a range of probabilities associated with certain long vacancy activities, efforts to incorporate other occupant behavior-related aspects such occupant schedules and thermal set points could further improve modeling efforts.

Numerical study of convection during night cooling and the implications for convection modeling in Building Energy Simulation models

When predicting the performance of night ventilation with Building Energy Simulation models, the results are highly sensitive to the selection of the convective heat transfer coefficient (CHTC). Therefore, a numerical study was conducted, simulating rooms with Fluent 12 for 8h of flow time. The study was limited to 2D and results must be considered as qualitative. 14 initial conditions were applied to 4 geometries, including the thermal mass in floor and ceiling. The ACH was varied from 4 to 11h−1 and initial ΔK between room air and supply air from 1.25 to 10K. The flow development showed three flow phases, influencing the CHTC at the ceiling. The implications for the selection of CHTC in BES are discussed and applied in a sensitivity study in TRNSYS 17. The sensitivity on the thermal comfort was investigated through a parameter variation, using convection correlations from literature. When comparing with the internal CHTC calculation in TRNSYS, the usage of natural convection correlations increased the predicted weighted overheating hours at 4 and 12 ACH respectively with 18Kh (19%) and 105Kh (12%), whereas forced convection correlations reduced this at 4 and 12 ACH respectively with 30Kh (11%) and 210Kh (29%).

Potential of SOFC CHP systems for energy-efficient commercial buildings

An energy-efficient and low-emissions solid oxide fuel cell (SOFC) combined heat and power (CHP) system is a promising electric and thermal energy generation technology for implementation in future commercial buildings.Recently developed performance models for kW- and MW-sized SOFC CHP systems have been integrated into a building energy simulation model of a medium-sized (7000m2) office building to evaluate the potential of the system to lower annual utility costs and to reduce CO2 emissions. An optimized 175kW SOFC CHP system successfully lowered annual utility costs by up to 14.5% over a baseline HVAC system in locations with space heating-dominant loads. Potential CO2 emissions were reduced by up to 62% over the baseline case for the optimized SOFC CHP systems.If the capital and installation costs of SOFC CHP systems are reduced in the near future, through the realization of cheaper, high-performance intermediate temperature SOFCs, the SOFC CHP system may be a promising energy-efficient and low-emitting alternative power and thermal energy cogeneration technology.

Quantification of Fossil Fuel CO2 Emissions on the Building/Street Scale for a Large U.S. City

In order to advance the scientific understanding of carbon exchange with the land surface, build an effective carbon monitoring system, and contribute to quantitatively based U.S. climate change policy interests, fine spatial and temporal quantification of fossil fuel CO(2) emissions, the primary greenhouse gas, is essential. Called the "Hestia Project", this research effort is the first to use bottom-up methods to quantify all fossil fuel CO(2) emissions down to the scale of individual buildings, road segments, and industrial/electricity production facilities on an hourly basis for an entire urban landscape. Here, we describe the methods used to quantify the on-site fossil fuel CO(2) emissions across the city of Indianapolis, IN. This effort combines a series of data sets and simulation tools such as a building energy simulation model, traffic data, power production reporting, and local air pollution reporting. The system is general enough to be applied to any large U.S. city and holds tremendous potential as a key component of a carbon-monitoring system in addition to enabling efficient greenhouse gas mitigation and planning. We compare the natural gas component of our fossil fuel CO(2) emissions estimate to consumption data provided by the local gas utility. At the zip code level, we achieve a bias-adjusted Pearson r correlation value of 0.92 (p < 0.001).

Performance evaluation of network airflow models for natural ventilation

The potential for natural ventilation in building design to reduce associated building cooling and fan energy consumption makes it an area of great interest. However, its increased use is dependent on the availability of accurate modeling tools to predict its impact on a given building design. Network airflow models have been developed to predict airflows within buildings and between indoor and outdoor, which usually require user-supplied thermal conditions in a space. Because of the dependency between airflow and space temperature in naturally ventilated buildings, network airflow models need to be incorporated into whole building energy simulation models. This study reviews existing network models that may be used for natural ventilation design and investigates the capability and accuracy of four prevalent network airflow models to simulate a wide range of natural ventilation scenarios, with varying combinations of geometry and natural ventilation driving forces. Comparison of predictions with measurements obtained from literature shows that the tools are generally able to predict natural ventilation rates within 35% except for the wind-driven single-sided ventilation case. The article indicates some significant needs for improvement of the network airflow model for natural ventilation applications.

Key factors methodology—A novel support to the decision making process of the building energy manager in defining optimal operation strategies

This paper presents the key factors methodology that supports energy managers in determining the optimal building operation strategy in relation to both energy consumption and thermal comfort. The methodology is supported by the utilization of calibrated building energy simulation models that match measured data gathered by an extensive measurement framework. Building management systems, do not allow energy managers to test the impact of changes in control settings of energy systems on energy consumption and thermal comfort. The proposed methodology and prototype tool chain enables energy managers to virtually develop and test the impact of proposed changes to the control settings in the building energy systems prior to their implementation in the physical building. The paper outlines the proposed methodology defining the underpinning concepts and illustrating the performance metrics required to capture the effect of different building operation strategies. A case study is discussed to demonstrate the application of the methodology.

Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor’s parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO 2 emissions if additional factors are considered.

Assessing the accuracy of a simplified building energy simulation model using BESTEST: The case study of Brazilian regulation

This paper reports the use of an internationally recognized validation and diagnostics procedure to test the fidelity of a simplified calculation method. The case study is the simplified model for calculation of energy performance of building envelopes, introduced by the Brazilian regulation for energy efficiency in commercial buildings. The first step of the assessment consisted on evaluating the simplified model results using the BESTEST. This paper presents a straightforward approach to apply the BESTEST in other climates than the original one (Denver, USA). The second step of the assessment consisted on applying the simplified model to evaluate four building typologies, and compare the results with those obtained using a state of the art building energy simulation (BES) program. For some BESTEST cases, the simplified model presented results inside of a confidence interval calculated by the authors. However, the simplified model was found to yield significant difference in the four building typologies analysed. Moreover, in all four building typologies analysed, the simplified model led to a lower energy efficiency label when compared to the label obtained using BES. The paper concludes that the simplified model may require improvements to properly indicate the actual energy performance of commercial building envelopes.

Adaptive comfort: Analysis and application of the main indices

Active climatization is currently one of the main causes of energy use in buildings. Since it aims at providing indoor environmental conditions that are comfortable for most of the building occupants, the way these conditions are determined is very important in the framework of energy optimization. Indoor comfort conditions are conventionally expressed in terms of steady temperature levels (e.g. 20–26 °C). Differently, the adaptive approach determines temperature levels that are unsteady and follow the variability of the outdoor climate. Even if this alternative approach has proven to be very effective in providing mitigated indoor temperatures, agreement about its formulation and its practical application is still lacking. In this paper some of the available formulations of the adaptive approach are described and adopted to determine comfort temperatures for three different Italian climatic contexts. Moreover, the discomfort levels for a case-study room are estimated, by the means of a dynamic building energy simulation model, according to both the conventional and the adaptive approaches.

Multi-Criteria optimisation using past, real time and predictive performance benchmarks

Performance based design, construction, commissioning and operation of buildings requires virtual testing and validation of project alternatives. In the case of environmental and energy management of buildings, whole Building Energy Simulation (BES) models can be used to determine indoor environmental conditions, building energy consumption, system performance, and associated CO 2 emissions, etc. BES is currently used at the design and commissioning phases of the Building Life Cycle (BLC) but not during the operational phase. This paper defines a methodology that incorporates predictive BES into building operation while acknowledging present technological limitations with respect to model accuracy and required resources. This predictive model also requires detailed definition and characterisation of inputs including: Historical data from buildings; Real time data such as measurements from meters and wired and wireless sensors underpinned by a Building Management System (BMS) and Future data such as short term weather forecast values and expected occupancy schedules. The paper concludes with a demonstration of the predictive BES model methodology using an existing building at University College Cork, Ireland.