Prototype Building Models Research Articles

Occupancy schedule development and its effect on OpenStudio prototype college building model

College buildings have unique characteristics compared with school buildings. Therefore, defining the realistic occupancy schedule in a prototype college building has significant research opportunities. In this study, the actual operating schedules of each space type were collected and generated based on the class reservation schedule and compared with the previous reference schedule (primary/secondary school). The schedules were analyzed for their effect on the OpenStudio prototype college building model. The findings highlight that the use of a typical school building schedule in a college building impairs the granularity of information. The analysis shows significant differences between the previous occupancy schedule and the updated occupancy schedule of the college building, leading to a considerable decrease in occupancy density. Furthermore, the effect of these occupancy pattern changes on the prototype building model is examined. The variations were observed in minimum ventilation requirements, the average mechanical ventilation rate, and energy consumption attributed to changes in occupancy density.

AutoBPS-Prototype: A web-based toolkit to automatically generate prototype building energy models with customizable efficiency values in China

Prototype building energy models are significant in building energy performance evaluation and energy efficiency potential analysis. However, their establishment process is time-consuming and error-prone. This study aims to develop a user-friendly toolkit, AutoBPS-Prototype, to automatically establish prototype building energy models in China. A web-based graphical user interface of AutoBPS-Prototype was designed and implemented. AutoBPS-Prototype only needs to define the climate zone, building type, and standard vintage to generate a default prototype building energy model based on a pre-built library. In addition, it can generate user-defined models through customized important efficiency values. The OpenStudio Standards used to create the U.S. Department of Energy prototype building models based on the ASHRAE 90.1 standards was extended to cover more prototype building energy models in China, including 20 sub-climate zones, 22 building types and four code and standard vintages. The Chinese building standards were used for the efficiency settings. Besides, case studies were conducted. Firstly, 20 prototype building models of four building types in five cities were generated. The credibility of the model results simulated by EnergyPlus was verified through comparison with measured data. Then, the impact of spatiotemporal differences on building energy consumption was analyzed through simulation of the models using different standard vintages in different climate zones. Finally, a baseline and four upgrade scenarios were designed to explore the impact of various upgrades on building energy consumption. This toolkit aims to assist users in conducting rapid research on building energy efficiency improvement. More toolkit features are under development for retrofit analysis, demand response analysis, and photovoltaics system generation estimation.

Grid-scale demand-side flexibility services using commercial buildings lighting loads

Flexibility Services can be used to account for the high variability in electricity generation due to increasing renewable energy sources such as wind and solar energy. As buildings become smarter with the adoption of new technologies for sensing and control, more integration between buildings and the electric grid is possible. Building loads such as air conditioning and lighting in commercial buildings have the potential to provide these Flexibility Services. In commercial buildings, lighting accounts for approximately 10–15 % of the load at time. Past studies have shown that lighting can be dimmed by 15–20 % without causing visual discomfort to the occupants. Overall, this study aims to estimate the instantaneous demand reduction that can be provided due to lighting loads using prototypical building models aggregated across electric grid nodes in the Midcontinent Independent System Operator (MISO) region in the United States. Findings suggest, in a future case with 100 % LED fixtures and 30 % technology penetration for smart lighting controls, lighting loads can provide around 250 to 475 MW (0.21 to 0.39 % of MISO’s peak load) of Flexibility Services in the MISO region. The results can be used as inputs for grid levels models to predict future generation, transmission, and distribution investments for future high renewable energy scenarios.

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m2), 10.0 W/m2, 11.1 W/m2, 7.1 W/m2, and 4.7 W/m2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. The comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

Chinese prototype building models for simulating the energy performance of the nationwide building stock

Chinese prototype building models for simulating the energy performance of the nationwide building stock

Multi-Objective Optimization of Daylight Performance and Thermal Comfort of Enclosed-Courtyard Rural Residence in a Cold Climate Zone, China

This study explored the daylight performance, thermal comfort, and energy-saving level of enclosed-courtyard rural residences with covered roofs in a cold IIA region of China. Using daylight autonomy (DA), annual mean value of PMV (AUMPMV), and total energy consumption of heating, cooling and lighting (Ehcl) in a performance evaluation index, a courtyard roof was optimized in a multi-objective fashion and analyzed for its effect on residential performance. This was achieved through field investigation and mapping, setting the prototype building model in the Rhino platform, and carrying out the iterative optimization design of the roof with a Ladybug tool component. The results show that appropriate roof form can significantly improve daylight performance, thermal comfort, and energy savings. Compared to the open-courtyard rural residence prototype model, the spatial daylight autonomy (sDA) of a flat-skylight-enclosed-courtyard residence decreased by only 0.94%, and AUMPMV improved significantly from −0.774 to 0.87. The Ehcl decreased by 19.7%, and compared with setting a large area of flat skylights on the roof, a reasonable clerestory window roof can achieve better residence performance.

Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting

Building energy consumption accounts for about 40% of global primary energy use and 30% of worldwide greenhouse gas (GHG) emissions. Among the energy-related factors present in buildings, heating, cooling, and air-conditioning (HVAC) systems are considered major contributors to whole-building energy use. To improve the energy efficiency of HVAC systems and mitigate whole-building energy consumption, accurately predicting the building energy consumption can play a significant role. Although many prediction approaches are available for building energy use, a machine learning-based modeling approach (i.e., black box models) has recently been considered to be one of the most promising building energy modeling techniques due to its simplicity and flexibility compared to physics-based modeling techniques (i.e., white box models). This study presents a building energy load forecasting method based on long-term short-term memory (LSTM) and transfer learning (TL) strategies. To implement this approach, this study first conducted raw data pre-processing analysis to generate input datasets. A hospital building type was considered for a case study in the first stage. The hospital prototype building model, developed by the U.S. department of energy (DOE), was used to generate an initial input training and testing dataset for source domain tasks before the transfer learning process. For the transfer learning process in a target domain, a simulation-based analysis was also conducted to obtain target datasets by assuming limited data lengths in different weather conditions. The training and testing procedures were performed using separate cooling and heating periods with and without the transfer learning process for source and target domain tasks, respectively. Lastly, a comparative analysis was carried out to investigate how the accuracy of LSTM prediction can be enhanced with the help of transfer learning strategies. The results from this study show that the developed LSTM-TL model can achieve better performance than the prediction model, which only uses LSTM under different weather conditions. In addition, accurate performance can vary according to different transfer learning methods with frozen and fine-tuning layers and locations.

Machine Learning Approach to Predict Building Thermal Load Considering Feature Variable Dimensions: An Office Building Case Study

An accurate and fast building load prediction model is critically important for guiding building energy system design, optimizing operational parameters, and balancing a power grid between energy supply and demand. A physics-based simulation tool is traditionally used to provide the building load demand; however, it is constrained by its complex model development process and requirement for engineering judgments. Machine learning algorithms (i.e., data-driven models) based on big data can bridge this gap. In this study, we used the massive energy data generated by a physics-based tool (EnergyPlus) to develop three data-driven models (i.e., LightGBM, random forest (RF), and long-short term memory (LSTM)) and compared their prediction performances. The physics-based models were developed using office prototype building models as baselines, and ranges were provided for selected key input parameters. Three different input feature dimensions (i.e., six-, nine-, and fifteen-input feature selections) were investigated, aiming to meet different demands for practical applications. We found that LightGBM significantly outperforms the RF and LSTM algorithms, not only with respect to prediction accuracy but also in regard to computation cost. The best prediction results show that the coefficient of variation of the root mean squared error (CVRMSE), squared correction coefficient (R2), and computation time are 5.25%, 0.9959, and 7.0 s for LightGBM, respectively, evidently better than the values for the algorithms based on RF (18.54%, 0.9482, and 44.6 s) and LSTM (22.06%, 0.9267, and 758.8 s). The findings demonstrate that a data-driven model is able to avoid the process of establishing a complicated physics-based model for predicting a building’s thermal load, with similar accuracy to that of a physics-based simulation tool.

Towards Low Energy Buildings through a Prototype of Desert Rural House in Alwadii Algadid in Egypt

Desert Vernacular architecture is one of the maximum important factors for the retrofit of the desert rural house. Vernacular architecture primarily based totally on increase thermal comfort and reducing energy consumption to improve indoor air quality inside the spaces. Alwadi Algadid (New Valley) Governorate establishes 46% of Egypt area. So, we should obtain profit of this greater zone to decrease the pressure over the Nile valley. In addition, this research aims to categorize the influence of desert vernacular architecture of desert rural house. Design/methodology/approach – The architecture of Alwadi Algadid contains the particular architectural form of a warm zone, which previously had an operational tradition aimed to a sustainable architecture and improvement through cultural, economic, geographical, and climatic criteria, to use the local materials, treatments within the basic principles of sustainability, at the lowest possible cost to minimize utilities running cost of these communities. The prototype house was designed by Design-Builder simulation software depend on an awareness of desert vernacular architecture. Findings – In conclusion, the simulation findings of the prototype building models were evaluated with the base case building with more than 50% reduction in energy consumption. The results gained in this research could supply valuable ideas for the desert rural house design of residential buildings. Practical implications – Applied of using local materials, local technique and local treatment to confirm district suitability of the desert vernacular house outcome. Originality/value – The value of this research is to design guidelines for future vernacular settlement of desert rural house and establishments with respect to old ones.

Impacts of Occupant Behavior on Building Energy Consumption and Energy Savings Analysis of Upgrading ASHRAE 90.1 Energy Efficiency Standards

Commercial prototype building models were developed by the United States Department of Energy (DOE) to analyze the energy savings of the ASHRAE 90.1 standard. However, in the DOE models, occupant behavior inputs were deterministic and the stochasticity of occupant behavior was not fully characterized. This study evaluated the impacts of stochastic occupant behavior on building energy consumption and energy savings analysis from upgrading the ASHRAE 90.1-2016 to 2019 in sixteen climate zones in the United States (U.S.). Three occupant behavior styles (austerity, normal, and wasteful) were defined to represent the different levels of energy consciousness. The DOE medium office prototype models were used as the baseline (normal behavior style). The occupant behavior functional mock-up unit (obFMU) was used to model the stochastic occupant behavior models (austerity and wasteful). The EnergyPlus medium office prototype models were modified to co-simulate with the obFMU models. The results of 16 climate zones were aggregated by the relative construction volume of each climate zone. The results showed that the weighted national annual source energy use intensity (EUI) of the austerity, normal, and wasteful behavior styles were 203.81 kWh/m2, 287.15 kWh/m2, and 388.33 kWh/m2 for ASHRAE 90.1-2016, and 192.43 kWh/m2, 273.48 kWh/m2, and 371.28 kWh/m2 for ASHRAE 90.1-2019, respectively. Compared to the normal behavior style, the austerity behavior style consumed 29% less source energy, while the wasteful behavior style consumed 36% more source energy. From upgrading the ASHRAE 90.1-2016 to 2019, the energy saving percentages based on the austerity, normal, and wasteful behavior styles were 5.59%, 4.78%, and 4.42%, respectively. The stochastic occupant behavior significantly impacted the building energy consumption, and their impacts on the energy savings analysis of upgrading ASHRAE 90.1 were also not negligible.

Using Regression Model to Develop Green Building Energy Simulation by BIM Tools

Energy consumption in the building sector poses a huge burden in terms of global energy and pollution. Recent advancements in building information modelling and simulating building energy performance (BEP) have provided opportunities for energy optimization. The use of building information modelling (BIM) also has increased significantly in the last decade based on the requirement to accommodate and manage data in buildings. By using the data, some building information modelling tools have developed the function of energy analysis. This paper aims to identify design parameters critical to BEP to assist architects in the initial stages of building design and to investigate their relationship. The outcomes of the prototype model’s energy simulations were then used to construct multilinear regression models. For the rest of the independent building design variables, linear regression models are used to analyse the relationship between it and energy consumption. It was concluded that, in the same building conditions, diamond-shaped buildings have the highest energy consumption, while triangle-shaped buildings showed the most efficient energy performance through energy simulations for seven fundamental prototype building models based on Autodesk Kits, Green Building Studio (GBS) with a Doe-2 engine. In addition, the developed regression models are validated to within 10% error via a case study of the ABS building. At the end of this paper, recommendations are provided on energy optimisation for the initial stages of building design. The parametric analysis of design variables in this study contributed to the total energy consumption at the early phases of design and recommendations on energy optimization.

Implementation of Open Data Exchange between Architectural Design and Structural Analysis Models

Building information modelling promises model-based collaboration between stakeholders in the project design stage. However, data exchange between physical and analytical building models used for architectural design and structural analysis respectively rarely takes place due to numerous differences in building element representation, especially the representation of geometry. This paper presents the realization of a novel data exchange framework between architectural design and structural analysis building models, based on open interpretations on central storage. The exchange is achieved with a new system architecture, where the program redDim was developed to perform the interpretations, including the most challenging transformations of geometry. We deliver a proof of concept for the novel framework with a prototype building model and verify it on two further building models. Results show that structural-analysis models can be correctly automatically created by reducing dimensionality and reconnecting building elements. The proposed data exchange provides a base for missing standardization of interpretations, which facilitates the non-proprietary automated conversion between physical and analytical models. This research fills the gap in the existing model-based communication that could lead to a seamless data exchange.

On the Benefits of Whole-building IAQ, Ventilation, Infiltration, and Energy Analysis Using Co-simulation between CONTAM and EnergyPlus

Publicly available tools to perform whole-building simulation of indoor air quality, ventilation, and energy have been available for several decades. Until recently, these tools were developed in isolation, such as the whole-building contaminant transport and airflow analysis tool, CONTAM, developed by the National Institute of Standards and Technology (NIST) and the whole-building energy analysis tool, EnergyPlus, developed by the U.S. Department of Energy (DOE). The ability to couple these tools during runtime has been implemented through co-simulation, enabling improved analysis of the interdependent effects of temperature and airflow on contaminant transport and energy use on a whole-building scale.This presentation will include the development of a set of coupled reference building models for the purposes of evaluating the potential benefits of using co-simulation between CONTAM and EnergyPlus. A set of Residential Prototype Building Models available from DOE has been modified by NIST and utilized to demonstrate the coupling process and the benefits of this coupling with respect to IAQ and energy analysis, and to evaluate multiple whole-building simulation methods related to infiltration, ventilation, and occupant exposure. These methods include an original EnergyPlus prototype model, the original model with NIST-based infiltration correlations, co-simulation between EnergyPlus and CONTAM, and stand-alone CONTAM simulations. Potential benefits will be explored related to the ability of co-simulation to address the effects of variations in building typology and ventilation system performance on contaminant transport results while leveraging the capabilities of whole-building energy analysis.

Delaying the temperature fluctuations through PCM integrated building walls—Room conditions, PCM placement, and temperature of the heat sources

AbstractPhase change materials (PCMs) are well accepted by the researchers to reduce the temperature fluctuation and also the cooling load of a building. Sodium sulfate decahydrate (Na2SO4.10H2O) is used as PCM and incorporated in the traditional building wall in a prototype building model. The experiments are carried out on hot sunny days to investigate the temperature variation inside the room with and without PCM. It has been found that PCM can easily reduce the fluctuation of the inside temperature, however, the temperature variation inside the room much is influenced by the opening of the door and window where convective heat transfer plays a major role. The study reveals that the maximum average temperature was reduced for 3°C with the use of PCM layer. The studies have been extended for an elevated temperature by incorporating a heat source where the building wall is set without PCM, with PCM slab and also encapsulated PCM. The results reveal that both the PCM slab and encapsulated PCM building wall can control the temperature as close to ambient temperature at the opposite side of the heat source. Moreover, it is found that encapsulated building walls give better performance as the overall wall thickness is kept as original wall thickness. In addition, the evenly placement with the encapsulation in a building wall could maintain the melting/freezing cycle of the PCM materials for better performance.

Italian prototype building models for urban scale building performance simulation

Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. The developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.

Impact on Renewable Design Requirements of Net-Zero Carbon Buildings under Potential Future Climate Scenarios

This paper presents an analysis to foresee renewable design requirement changes of net- zero carbon buildings (NZCBs) under different scenarios of potential future climate scenarios in the U.S. Northeast and Midwest regions. A climate change model is developed in this study using the Gaussian random distribution method with monthly temperature changes over the whole Northeast and Midwest regions, which are predicted based on a high greenhouse gas (GHG) emission scenario (i.e., the representative concentration pathways (RCP) 8.5). To reflect the adoption of NZCBs potential in future, this study also considers two representative future climate scenarios in the 2050s and 2080s of climate change years in the U.S. Northeast and Midwest regions. An office prototype building model integrates with an on-site photovoltaics (PV) power generation system to evaluate NZCB performance under the climate change scenarios with an assumption of a net-metering electricity purchase agreement. Appropriate capacities of the on-site PV system needed to reach NZCB balances are determined based on the building energy consumption impacted by the simulated climate scenarios. Results from this study demonstrated the emission by electricity consumption increases as moving toward the future scenarios of up to about 25 tons of CO2-eq (i.e., about 14% of the total CO2-eq produced by the electricity energy source) and the PV installation capacity to offset the emission account for the electricity consumption increases significantly up to about 40 kWp (i.e., up to more than 10% of total PV installation capacities) as the different climate scenarios are applied. It is concluded that the cooling energy consumption of office building models would significantly impact GHG emission as future climate scenarios are considered. Consequently, designers of NZCBs should consider high performance cooling energy systems in their designs to reduce the renewable energy generation system capacity to achieve net-zero carbon emission goals.

Exploring potential for residential energy saving in New York using developed lightweight prototypical building models based on survey data in the past decades

In this research, a method of developing a model for predicting the energy saving potential of regional residential buildings has been presented. This model is predicated on a bottom-up methodology in representing and understanding the physical properties of the residential buildings in the region. The Residential Energy Consumption Survey (RECS) database is taken advantage to provide modeling inputs for the building modeling of the five developed archetypical building types. Different secondary energy supply systems are then distinguished and modeled by a developed lightweight building simulation tool - SimBldPy. The model is calibrated and validated based on energy use data from 1993 to 2015. It is It is used for the optimization of eleven selected energy conservation measures (ECM) to find the best potential scenario for equivalent primary energy for residential buildings in New York state (NY). Best combinations of ECM parameters are discerned for each building type by Sobol sensitivity analysis. The optimal regional energy use scenario is found after coupling the population projection. It is indicated that the saving potentials of the state-level primary energy use for different building types range from 48 % to 62 % compared with the baseline scenario at the end of 2040 in NY.

An assessment of power flexibility from commercial building cooling systems in the United States

Understanding the varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning loads in commercial buildings in the U.S. using a simulation-based method. Commercial buildings are first grouped by building type and climate region. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that cooling loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while medium office buildings have the most flexibility as a percentage of the rated power consumption.

How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study

Thermostat management plays a significant role in household energy conservation. This study aims to conduct a systematic and comprehensive analysis to quantify the energy savings potential of the occupant-centric smart thermostat based on a large-scale nationwide simulation infrastructure. The single-family Residential Prototype Building Model was used to represent a typical single-family detached house in the U.S. A generalized random occupancy presence schedule was created based on an occupancy probability schedule and k-means clustering algorithm. A total of 16,000 simulations, which were composed of four building foundation types, four heating source types, 40 American cities, five building energy code versions, and five thermostat control strategies, were conducted to evaluate the performances of the smart home thermostat in terms of saving building energy usage and maintaining occupant thermal comfort. The nationwide simulation results suggested that the temperature setback control during the unoccupied period could achieve some energy savings in the U.S. households. However, only very few of the 40 cities could see an annual Heating, Ventilation, and Air-conditioning energy savings ratio of over 30%. Besides, the implementation of the occupied standby temperature reset could greatly increase the peak load of the HVAC system and contribute to the grid load imbalance issue. It’s also worth noting that the smart recovery feature is proved to be able to bring additional benefits for a smart home thermostat. It could decrease the temperature setpoint not met time by about 30 min, and relieve the thermal discomfort due to the temperature setback control.

Prototype energy models for data centers

Data centers in the United States consume about twopercent of the nation’s electricity. Because heat gains from IT equipment drive cooling demand, data centers offer unique opportunities for energy savings. However, no prototype energy model for data centers is available in the suite of existing U.S. Department of Energy’s Commercial Prototype Building Models. This study presented the development of two new data center prototype models and their implementation in OpenStudio and EnergyPlus. The small-size data center model represents a computer room in a building served by computer room air conditioners (CRACs); while the large-sized model represents stand-alone data centers served by computer room air handlers (CRAHs) with a central chiller plant. For each data center model, two levels of IT equipment (ITE) load density were considered, to cover the wide range of IT power density of data centers: 40 and 100 W/ft2 (430 and 1076 W/m2) for the computer room, and 100 and 500 W/ft2 (1076 and 5382 W/m2) for the stand-alone data center. All other assumptions, such as building envelope, lighting, HVAC efficiencies and schedules, were based on the minimal requirements of ASHRAE Standard 90.1 at various vintages. We introduced a novel concept of supply and return air approach temperatures to capture the essential effects of non-uniform airflow and temperature distribution in data centers. The approach temperatures were pre-computed by computational fluid dynamics (CFD) simulations for various configurations of ITE loads and airflow containment management in data centers. A new feature was developed in EnergyPlus to implement the approach temperature method. A case study was conducted to demonstrate the use of the data center models. The two data center models cover all U.S. climate zones and can be used to evaluate energy saving measures for data centers, as well as to support development of data center energy efficiency codes and standards.