Automatic Building Energy Modeling (AutoBEM)

Is there a specific list of measures that we can pre-download?

• No. Measures are already in the GitLab AutoBEM scratch folder. If additional measures are needed, they can be download/created if needed.

Where can I look to find new/supported measures?

• Check Openstudio github repository

How do you differentiate between different buildings?

• A key is used for each building type and standard. AutoBEM uses this key to create the OSM. Ex. FullServicerestaurant90.1-2007

How do you modify parameters for different buildings?

• Currently AutoBEM is used for large scale simulations, so it uses the same building characteristics for a specific building type + standard combination. Subcategories are a work in progress and can be modified by the user.

What building information do you need to run AutoBEM?

• ID, State, CZ, Area2D, Centroid, Footprint2D, Height, BuildingType, WWR_surfaces, NumFloors, Area, Standard

What projection is used for Footprint2D?

• EPSG:4326

What are the units for area/height?

• Sqft/ft

How can AutoBEM be modified for individual building simulations?

• Change the identifier key in HVAC_space_types.json to the building ID, and modify the AutoBEM code to use the ID as the lookup.

How do I use specific measures in AutoBEM?

• AutoBEM allows the implementation of various measures, such as ‘Set Heat Pump Water Heater’ and ‘ReduceSpaceInfiltrationByPercentage’. These measures can be accessed and modified in the measures directory, and users may need to edit corresponding XML or script files.

Can AutoBEM simulate different heating fuel types?

• Yes, AutoBEM can simulate different heating fuel types. This may require modifying the central AC definition or using specific measures like ‘gas_to_electric_hvac’ for conversion.

What heating model is primarily used for residential buildings in AutoBEM?

• In AutoBEM, for residential buildings, the model used is “Central AC,”

What is the role of the ‘replacement.idf’ file in specific project contexts?

• The ‘replacement.idf’ file likely contains configurations or modifications specific to certain projects, particularly concerning electric heating outputs.

How do the measures directory as referenced by generate_geometry_CZ_THETA.py and the scratch/measures directory referenced by scratch/run_measure_on_osm_files.py differ?

• The measures directory as referenced by generate_geometry_CZ_THETA.py is the default setting for AutoBEM and the scratch/measures directory referenced by scratch/run_measure_on_osm_files.py is the directory for adding any measure which you may create/add/modify for specific projects.

What is the purpose of the measures directory in AutoBEM?

• The measures directory in AutoBEM is used for implementing specific energy-saving measures or modifications in the building models. This includes standard measures and project-specific customizations.

How can new measures be added or modified for specific projects in AutoBEM?

• New measures for specific projects can be added or modified in a particular directory, allowing for customization or creation of measures tailored to project-specific needs.

Can a residential building in AutoBEM be specified to exclusively use electric or gas heating in the central AC model?

• Yes, it is possible to specify a residential building to use exclusively electric or gas heating in the central AC model, but this requires changing the central AC definition, which is not typically recommended.

Is the ‘gas_to_electric_hvac’ measure in the scratch/measures directory relevant for changing a building’s heating type from gas to electric?

• Yes, using the ‘gas_to_electric_hvac’ measure from the scratch/measures directory could be an easier way to switch a building’s heating type from gas to electric.

For implementing measures such as Air sealing & weatherization, ASHP, heat pump water heaters, and LED retrofits, is it necessary to create these measures?

• Yes, these measures are not existing in AutoBEM and would need to be created, possibly using tools like OpenStudio.

Can a solar measure be modeled using the “apply_solar_inverter” in AutoBEM?

• Yes, solar measures can be modeled using “apply_solar_inverter,” but it’s important to double-check the arguments for using this measure to ensure accuracy.

What is the best way to model energy star appliances in AutoBEM?

• Modeling energy star appliances in AutoBEM is complex and requires understanding which HVAC systems are related to energy-star appliances. It’s not just about applying measures like smart thermostats and smart water heaters; specific measure development and customization from a model development perspective are necessary.

What tools are recommended for creating custom CSV files for AutoBEM?

• To create custom CSV files for AutoBEM, especially for getting building shapefiles given coordinates or an address, the use of external tools like geopandas is recommended.

What software or method is recommended for editing infiltration rates in AutoBEM?

• For changing infiltration rates, it’s suggested to use either Eppy for individual editing or a specific measure (like ‘ReduceSpaceInfiltrationByPercentage’) for large group editing.

Where can the default .idd file be found for use with AutoBEM and Eppy?

• The default .idd file for AutoBEM can be found in the installation directory of OpenStudio, typically under a path like /openstudio-3.2.0/EnergyPlus/Energy+.idd.

How can new measures like ASHP, air sealing, heat pump water heaters, and LED retrofits be implemented in AutoBEM?

• To implement new measures such as ASHP, air sealing, heat pump water heaters, and LED retrofits in AutoBEM, it might require creating additional measures using OpenStudio or adjusting parameters using Eppy.

How can shapefiles be sourced for the AutoBEM workflow?

• To source shapefiles for the AutoBEM workflow, one approach is to perform a spatial join between coordinates and the MAv1/v2 Centroid latitude/longitude to obtain the MA data row. From there, the Footprint2D column can be interpreted as a shapefile, and the WWR (Window-to-Wall Ratio) property associated with the row can be assigned.

What is the method for imputing or looking up Footprint2D and WWR surfaces in AutoBEM?

• For imputing or looking up Footprint2D and WWR surfaces in AutoBEM, the suggested method involves spatially joining coordinates with relevant centroid data to identify corresponding rows and then interpreting these rows to define shapefiles and assign WWR properties.

• Project overview
  • Ingraham, James A. and New, Joshua R. (2018). “Virtual EPB.” Presented to Building Technologies Office following the BTO Peer Review, 87 slides. Arlington, VA, May 3, 2018. [PPT]
• Savings at utility-scale
  • Bass, Brett, New, Joshua R., and Copeland, William (2020). “Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area.” Energies journal, Special Issue “Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings,” volume 14(1), issue 132, doi.org/10.3390/en14010132, Dec. 29, 2020. [Energies] [PDF] [OSTI]
• Data and algorithms
  • New, Joshua R., Adams, Mark, Garrison, Eric, Bass, Brett and Guo, Tianjing. (2020). “Scaling Beyond Tax Assessor Data.” ASHRAE/IBPSA-USA 2020 Building Performance Analysis Conference & SimBuild (BPACS), Chicago, IL, Sept. 29 – Oct. 1, 2020. [PDF] [PPT] [MP4]
• Smart-meter quality control
  • Garrison, Eric and New, Joshua R. (2021). “Quality Control Methods for Advanced Metering Infrastructure Data.” Smart Cities journal, Special Issue “Applied Artificial Intelligence in Energy Systems,” volume 4(1), pages 195-203, doi.org/10.3390/smartcities4010012, Jan. 28, 2021. [SmartCities] [PDF]

Measures Supported by AutoBEM

Geometry_from_lat_lon_points:

Purpose of the Measure:

This measure aims to generate building geometry in OpenStudio based on given latitude and longitude points.

Main Components and Actions:

1. Creating Spaces from Floor Prints (space_from_floor_print Method): This method constructs building spaces based on floor prints and other parameters:
   • It takes in parameters like the model, floor print (a list of points), floor height, window-to-wall ratio surfaces (wwr_surfaces), and a flag indicating if a surface is sub-surface.
   • The method constructs the floor of the space using the provided floor print points.
   • It then iterates over the floor print points to construct walls, setting the window-to-wall ratio for each wall based on the provided wwr_surfaces.
   • The method concludes by creating a roof or ceiling for the space using the reversed floor print points.
2. Outcome: By the end of this measure’s execution, the user will have an OpenStudio model with spaces representing the building’s geometry. The geometry is constructed based on latitude and longitude points provided as input, and the spaces have defined floors, walls (with potential windows based on the window-to-wall ratio), and roofs/ceilings.

This measure is crucial for converting simple 2D geographical data (lat-lon points) into a 3D representation of a building in OpenStudio, facilitating further energy modeling and simulation.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

Create_Virtual_epb:

Purpose of the Measure:

The create_virtual_epb measure seems to be aimed at creating or modifying a building model in OpenStudio to align with certain energy standards and specifications. It may also involve the addition or modification of HVAC systems.

Create Prototype Model (model_create_prototype_model Method):

• This is a central method in the measure.
• It appears to create a prototype model based on certain standards and the building’s type.
• The method makes use of various helper functions to:
  • Set outdoor air controls.
  • Modify HVAC systems.
  • Apply HVAC efficiency standards.
  • Adjust various parameters based on the prototype’s needs.
• There’s also mention of adding daylighting controls, adjusting exhaust fan efficiency, renaming nodes in air and plant loops, and removing unused objects.
• The method seems to culminate in the creation or modification of an OpenStudio model that is tailored to represent a specific building type under a specific energy standard.

Overall Purpose:

It is designed to transform an OpenStudio building model into a standardized virtual representation. It ensures that the building aligns with certain energy standards, incorporates the right HVAC systems, and embodies the characteristics of its intended building type (e.g., Highrise Apartment, Large Hotel etc.).

In essence, this measure acts as a bridge, converting a basic building model into a detailed and standardized energy model that can be used for more accurate energy simulations and analyses.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

Apply_output_control_files:

Purpose of the Measure:

The apply_output_control_files measure controls the types of output files generated by EnergyPlus after a simulation run. By default, EnergyPlus can produce a plethora of output files, many of which may not be needed for specific analyses. This measure allows users to fine-tune their output, focusing only on the data they need, which can save disk space and improve post-processing efficiency.

This measure streamlines the simulation output by allowing users to enable or disable specific output file types. In particular, it provides an option to produce native CSV outputs and disables the generation of many other output file types.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

RemoveLatentRadiantHeatingFromEquipment:

Purpose of the Measure:

This measure is designed to adjust the latent, radiant, and lost heat attributes of both electric and gas equipment in an OpenStudio model. By allowing users to specify these attributes, the measure provides a means to fine-tune the heat gain contributions from equipment, ensuring a more accurate representation in energy simulations.

Such adjustments are critical in scenarios where the heat gain from equipment can significantly impact space conditions, energy consumption, and HVAC loads. By ensuring that equipment heat gain is represented accurately, the measure helps to provide more reliable simulation results.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

ResContructions:

Purpose of the Measure:

The ResConstructions measure appears to be designed to modify specific constructions and materials within an OpenStudio model. In particular, it focuses on:

• Adjusting properties of materials, specifically a concrete floor material, to represent plywood.
• Assigning specific constructions to floor and ceiling surfaces.

The primary function seems to be standardizing or adjusting the model’s representations of floors and ceilings, ensuring they have the desired constructions and materials. Such adjustments are crucial when modeling residential buildings, where specific construction types might significantly influence the building’s thermal performance and energy use.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

SurfaceMatching:

Purpose of the Measure:

This measure is designed to streamline and automate the process of matching surfaces in an OpenStudio model. Proper surface matching is essential for accurate energy simulations, as adjacent surfaces share thermal properties and affect each other’s heat transfer.

This measure ensures that surfaces in the model that should be paired (like walls of adjacent rooms) are correctly matched. Optionally, before matching, it can also intersect surfaces, which divides surfaces where they overlap, ensuring more precise geometry representations.

By automating these processes, the measure aids in refining the model’s geometry representation, improving the accuracy and reliability of subsequent energy simulations.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

AddResGasEquipment:

Purpose of the Measure:

The measure’s name suggests a broader scope—adding residential gas equipment to the model. It is focused on defining a new gas equipment object and adding it to the OpenStudio model.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

set_building_type_data_energyplus_daysql:

Purpose of the Measure:

This measure seems to be primarily focused on manipulating the presence of certain objects within an EnergyPlus input or output file.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

set_building_type_data_GSHP:

Purpose of the Measure:

This measure appears to be focused on manipulating specific objects related to Energy Management Systems (EMS) and outdoor air design specifications within an OpenStudio model. It achieves two main tasks:

1. Modifying parameters related to outdoor air design specifications. This could potentially influence ventilation rates, outdoor air conditions, or other related attributes.
2. Removing a range of EMS objects from the model. This could be to simplify the model, remove potentially conflicting or redundant EMS scripts, or prepare the model for a specific type of analysis.

Given the measure’s name, this measure isrelated to Ground Source Heat Pumps (GSHPs).

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/geometry_generation/measures

Generate_EV_load_profile:

Purpose of the Measure:

This measure is likely focused on generating electric vehicle (EV) load profiles. Load profiles represent the electrical demand of EVs over time, which can be influenced by factors like charging times, battery capacities, and control strategies.

Detailed Summary:

1. Inverse Normal Truncated Function (inverse_normal_truncated Function):
   • This function seems to generate a time value based on a normal distribution, possibly with truncation. It uses the average (avg), standard deviation (std), and control parameters to determine the time value. The control parameter appears to influence how the time value is adjusted or truncated.
2. Inverse Normal Function (inverse_normal Function):
   • This function calculates a time value using an inverse normal distribution based on the provided average (avg) and standard deviation (std).
3. Pick Time Function (pick_time Function):
   • This function uses the inverse_normal function to select a time value. It may optionally restrict the time value within specified upper and lower bounds.
4. Check Control Scenario Function (check_control_scenario Function):
   • This function checks and adjusts the duration based on the provided control scenario. The control scenarios include “No Control”, “Partial Control”, and “Full Control”, each influencing the duration differently.
5. Make Monthly Load Function (make_monthly_load Function):
   • This function appears to generate a monthly load profile for electric vehicles (EVs). It seems to take into account different month lengths and creates an overall EV load profile that’s appended to a global list.
6. Main Execution Block:
   • This section starts by defining control scenarios and initializing various lists.
   • It reads a CSV file named baseline_2040.csv, which likely contains baseline monthly peak values.
   • The script then iterates over the monthly peak values, generating an overall EV load profile for each month.
   • The load profiles are stored in a table, load_table.
   • Finally, the script writes the load_table to a new CSV file, named based on the control scenario (e.g., peak_w_EV_No Control_2040.csv).

Overall Purpose:

The generate_EV_load_profile measure is designed to create electric vehicle (EV) load profiles, which represent the electrical demand of EVs over time. It does so by:

1. Utilizing various statistical functions to determine charging times based on control scenarios.
2. Reading baseline peak values from an existing CSV.
3. Generating monthly EV load profiles based on the control scenarios.
4. Writing the generated load profiles to a new CSV file.

This measure aids in simulating the impact of EV charging on a building or grid’s electrical demand, which is essential for energy modeling, grid planning, and understanding potential infrastructure challenges.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

apply_dual_fuel_replacement:

Purpose of the Measure:

Switching to dual fuel HVAC system so that in heating season, gas furnace will be used at peak hours instead of heat pump. The goal is to minimize electrical usage in winter. This measure consists of 3 python files.

The get_peak_heating_consumptions_monthlymax.py script focuses on processing and analyzing hourly heating consumption data across multiple files. Its main objectives are:

1. Aggregate hourly heating data across multiple premises or scenarios.
2. Identify and record the peak monthly heating consumption values.
3. Output the results to a CSV file for further analysis or reporting.

This process aids in understanding the heating demand patterns, especially in the context of the dual-fuel HVAC system. By identifying peak heating times, one can better strategize the operation of dual-fuel systems to minimize electricity usage during peak periods.

The large_scale_simulation.py script sets up and manages a large-scale simulation environment for running EnergyPlus simulations with a dual-fuel replacement strategy.

Key functionalities include:

1. Path Management: The script ensures that paths are properly formatted based on the operating system and sets up paths for critical directories and files.
2. Simulation Commands Setup: For each .idf file in the specified directory, the script constructs a command to run the large_scale_simulation.exe executable. This executable likely performs the actual EnergyPlus simulation.
3. Simulations Execution: Each constructed command is executed in sequence, running the simulations for each .idf file with the specified parameters.

The primary goal seems to be automating the process of running numerous EnergyPlus simulations, especially in the context of the dual-fuel replacement strategy. By automating this process, the script facilitates efficient and consistent analyses across multiple scenarios or building configurations.

The get_peak_heating_consumptions_monthlymax_parallel.py script focuses on processing and analyzing hourly heating consumptions stored in .pkl.gz files. Its main objectives are:

1. Parallel Processing: Utilize multiple CPU cores to process .pkl.gz files simultaneously. This is essential for efficiency when dealing with a vast number of files.
2. Data Extraction: For each .pkl.gz file, extract the hourly heating electricity consumption, especially for specified monthly peak times.
3. Output Compilation: Aggregate and output the results to a CSV file for further analysis or reporting.

By identifying hourly heating consumptions, especially during peak periods, the script aids in understanding heating demand patterns, particularly in the context of the dual-fuel HVAC system.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

apply_monthly_smart_thermostat:

Purpose of the Measure:

This measure appears to adjust thermostat settings based on user-specified criteria. The core functionality of the ApplyMonthlySmartThermostat measure is encapsulated within the run method and the auxiliary smart_thermostat method. Let’s delve into their functionalities:

Detailed Summary:

1. Smart Thermostat Adjustments (smart_thermostat Method):
   • This method adjusts thermostat settings based on a smart thermostat strategy.
   • Parameters include heating and cooling schedule rules, peak time, offset value, pre and post peak hours, output file paths, thermostat name, building information, and monthly datetime.
   • The method begins by checking if both the heating and cooling schedule rules are not provided (nil); if so, it returns false.
   • For the heating and cooling rules, the method sets the application days (Sunday through Saturday) and extracts the day schedule.
   • The method adjusts the thermostat settings based on the offset and peak hours, modifying the heating and cooling schedules accordingly.
   • The adjusted thermostat values are stored in the cooling and heating arrays.
2. Main Execution (run Method):
   • This method is the primary execution block of the measure.
   • The method begins by extracting the provided arguments and setting up necessary paths and variables.
   • It then retrieves all thermostats in the model.
   • For each thermostat, the method retrieves the heating and cooling setpoint schedules.
   • For each setpoint schedule, the method checks if the schedule has been processed (i.e., adjusted) already.
   • If not processed, the method calls the smart_thermostat function to adjust the thermostat settings based on the smart thermostat strategy.
   • The adjusted thermostat values are appended to the cooling_setpoints and heating_setpoints arrays.
   • Finally, the method writes the zoning information and adjusted thermostat values to output CSV files.

Overall Purpose:

The ApplyMonthlySmartThermostat measure focuses on implementing a smart thermostat strategy in an OpenStudio model. The measure adjusts thermostat settings based on user-specified criteria, which include peak times, offset values, pre and post peak hours, and other related parameters.

The primary goals of the measure include:

1. Thermostat Adjustments: Modify thermostat settings in the OpenStudio model based on a smart strategy to achieve energy savings, demand response, or specific comfort conditions.
2. Data Extraction: Extract and store the adjusted thermostat values, which can be used for further analysis or for verification of the applied strategy.
3. Output Generation: Write the adjusted thermostat values and zoning information to output CSV files for easy access and review.

By adjusting the thermostat settings on a monthly basis, the measure aims to respond to varying conditions and requirements, ensuring optimal performance and energy efficiency throughout the year.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

apply_monthly_smart_water_heater:

Purpose of the Measure:

The ApplyMonthlySmartWaterHeater measure focuses on adjusting water heater setpoint schedules in an OpenStudio model based on user-specified peak times. The adjustments aim to implement a smart water heater strategy, likely to achieve energy savings, demand response, or specific operational criteria.

Key functionalities of the measure include:

1. Water Heater Adjustments: Modify the water heater’s setpoint temperature schedule in the OpenStudio model based on a smart strategy. This is done for each specified monthly peak time.
2. Efficient Processing: To prevent redundant processing, the measure keeps track of already processed water heater schedules.
3. Error Handling: The measure incorporates multiple checks to handle potential errors, such as missing or invalid user inputs and unexpected model configurations.

By adjusting the water heater’s operational schedule based on specific criteria, the measure aims to optimize the water heater’s performance and energy consumption throughout different times of the year.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

apply_solar_inverter:

Purpose of the Measure:

This measure, which stands for “PV Estimation”, focuses on integrating PV generation capabilities into an OpenStudio model. This measure aims to estimate the potential of photovoltaic (PV) systems based on provided parameters like roof ratios, street azimuths, system sizes, and surface fractions.

Key functionalities of the measure include:

1. PV System Setup: Define and set up the PV generation system in the model using OpenStudio’s PVWatts capabilities.
2. Parameter Adjustments: Adjust various parameters, such as azimuth and tilt angles, to optimize the PV system’s orientation and capture the maximum solar radiation.
3. Capacity Estimation: Calculate the system capacity based on factors like roof area and module efficiency.
4. Model Update: Update the OpenStudio model with the newly created PVWatts generator objects, effectively simulating the presence of solar panels on the building.

By integrating these PV capabilities into the model, the measure enables users to assess the potential solar energy generation for a building and make informed decisions regarding the feasibility and benefits of solar energy adoption.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

change_windows_to_simple:

Purpose of the Measure:

The ChangeWindowsToSimple measure is designed to simplify the window modeling in an OpenStudio model by converting detailed or complex window constructions into a single-layered “simple glazing” construction.

Key functionalities of the measure include:

1. Window Simplification: The measure replaces multi-layered window constructions with a single-layer representation, using the Simple Glazing method in OpenStudio.
2. Thermal Performance Definition: The measure requires the U-Factor and Solar Heat Gain Coefficient (SHGC) as inputs, which are fundamental metrics for defining the thermal performance of windows.
3. Model Update: The measure updates the model’s default window construction, ensuring that all windows in the model will utilize the simplified construction.

By simplifying the window modeling approach, the measure can help streamline simulations, especially in preliminary analyses where detailed window characteristics might not be necessary. It offers a way to quickly gauge the impact of basic window performance metrics on a building’s energy performance.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

gas_to_electric_hvac:

Purpose of the Measure:

This measure focuses on converting or transitioning HVAC systems in an OpenStudio model from gas-fueled systems to electric systems.

Create Coil (create_coil_heating_dx_single_speed Method):

• This method is responsible for creating a direct expansion (DX) single-speed heating coil for electric HVAC systems.
• Several performance curves are defined for the coil, which characterize its behavior under varying conditions. These curves include:
  • totalHeatingCapacityFunctionofTemperatureCurve: Describes the coil’s heating capacity as a function of temperature.
  • totalHeatingCapacityFunctionofFlowFractionCurve: Defines the heating capacity relative to the flow fraction.
  • energyInputRatioFunctionofTemperatureCurve: Represents the energy input ratio of the coil with respect to temperature.
  • partLoadFractionCorrelationCurve: Provides the relationship between part-load conditions and performance.
  • defrostEnergyInputRatioFunctionofTemperature: Specifies the defrost energy input ratio in relation to temperature.
• Once these curves are defined, the method creates a CoilHeatingDXSingleSpeed object, which represents the electric heating coil.
• Several properties of the coil are set, including its rated coefficient of performance (COP), defrost strategy, and defrost control.
• The coil object is returned at the end of the method.

Purpose of the Measure:

This measure appears to be designed to transition HVAC systems in an OpenStudio model from gas-fueled systems to electric systems. While we’ve only examined a subset of the measure’s functionality, it’s clear that one of its actions is to introduce a DX single-speed heating coil that operates on electricity.

Key functionalities of the measure include:

1. Coil Creation: Defines and sets up an electric DX heating coil in the model, characterized by various performance curves.
2. Performance Curves: Establishes the behavior of the electric coil under different conditions, ensuring accurate simulation results.
3. Model Update: In the main execution method, the measure replaces gas-fueled HVAC components with electric components, like the defined coil.

The measure aims to support electrification efforts by allowing users to simulate a building’s performance with electric HVAC systems, as opposed to gas systems.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

microgrid_resiliency:

Purpose of the Measure:

This Measure starts with reading in lists containing building IDs in each microgrid. The measure extracts the hourly electricity usage for all basline buildings, then group the hourly data by each data. The hourly electric consumptions for baseline buildings comes from the meter file from the corresponding baseline idf run. The first part is to simply sum up the total hourly electric usage to determine the potential size of electric storage need to be installed for each microgrid. Later on this measure will integrate alternative energy source in to the grid distribution system.

How to run microgrid measure:

1. After running the baseline models, put all the run folders in ‘/results’ folder
2. Run ‘getHourlyProfile.py’
3. The hourly energy consumption profile for each microgrid will be stored in the ‘compiled_results’ folder

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/scratch/measures

ReduceSpaceInfiltrationByPercentage:

Purpose of the Measure:

This measure focuses on adjusting the infiltration rates of spaces in an OpenStudio model based on a user-specified percentage. Infiltration rates influence the building’s heating and cooling loads, and adjusting these rates can lead to energy savings and improved indoor conditions.

Key functionalities of the measure include:

1. Infiltration Adjustments: Modify the space infiltration rates in the model based on user-defined percentages.
2. Coefficient Updates: Update the coefficients that dictate how infiltration rates change with temperature differences, wind speeds, and other factors.
3. Lifecycle Cost Analysis: Compute the costs associated with the infiltration adjustments, aiding in decision-making for potential retrofit or design strategies.

By offering a streamlined way to adjust infiltration rates, the measure helps users understand the energy implications of different infiltration scenarios and make informed decisions regarding building design or retrofit interventions.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/deliverable/measures

Set Heat Pump Water Heater:

Purpose of the Measure:

This measure focuses on modeling, adjusting, or implementing heat pump water heaters in residential buildings within an OpenStudio model. Heat pump water heaters are known for their efficiency and can provide significant energy savings compared to conventional water heaters.

Key functionalities of the measure include:

1. HPWH Configuration: Adjusts various properties and components of the heat pump water heater, such as the tank and associated fan.
2. EMS Interaction: Uses an Energy Management System program to handle interactions between the HPWH and the living space, ensuring accurate simulations of temperature and humidity effects.
3. Performance Monitoring: Retrieves various output variables to monitor and report on the HPWH’s performance and its impact on the building’s conditions.

This measure allows users to model and simulate the effects of introducing or adjusting a heat pump water heater in a residential building. By offering detailed configuration options, users can explore various scenarios and strategies related to HPWH implementation and operation.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/deliverable/measures

Set R-value of Insulation for Roofs to a Specific Value:

Purpose of the Measure:

This measure focuses on adjusting the R-value of roof insulation in an OpenStudio model to a specific user-defined value. By modifying the R-value, the thermal resistance of the roof is altered, which can have a significant impact on the building’s overall energy performance.

Key functionalities of the measure include:

1. R-value Adjustment: Adjusts the R-value of roof insulation based on user input.
2. Economic Considerations: Allows users to specify costs associated with the insulation retrofit, enabling lifecycle cost analyses.
3. Validation: Ensures provided arguments are within reasonable bounds and issues warnings for potential outliers.
4. Utility Function: Offers a utility to format numbers for improved readability in outputs and reports.

By providing a structured approach to adjust roof insulation properties, the measure assists users in simulating the effects of insulation retrofit projects. This aids in understanding potential energy savings and economic implications of such interventions.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/deliverable/measures

Set Space Infiltration by Exterior Surface Area:

Purpose of the Measure:

This measure focuses on adjusting the infiltration rates of spaces in an OpenStudio model based on the exterior surface area of those spaces. This allows for more nuanced infiltration modeling, where larger spaces with more exterior surface area might have higher infiltration rates compared to smaller spaces.

Key functionalities of the measure include:

1. Infiltration Adjustment: Modifies the infiltration rates based on the exterior surface area of spaces.
2. Economic Considerations: Accounts for costs related to the adjustments, such as material, installation, and O&M costs.
3. Validation: Ensures provided arguments are within reasonable bounds and issues warnings for potential outliers.
4. Utility Function: Offers a utility to format numbers for improved readability in outputs and reports.

By offering a structured approach to adjust infiltration rates based on exterior surface areas, the measure helps users model more realistic scenarios, especially in complex buildings with varying space sizes and configurations.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/deliverable/measures

adjust_lighting_power_density:

Purpose of the Measure:

This measure focus on adding a new space to the OpenStudio model with a user-specified name. However, it’s a bit puzzling because the name and description of the measure suggest that it should adjust the lighting power density of spaces, but the main execution appears to focus on adding a new space rather than adjusting LPD values.

It’s possible that there’s more to the measure than is initially apparent, or that the measure might be a work-in-progress or part of a broader workflow.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/deliverable/measures

convert_to_electric_hvac:

Purpose of the Measure:

The ConvertToElectricHVAC measure, based on the visible code sections, aims to convert HVAC systems in an OpenStudio model to electric-powered systems. One evident component is the creation of a direct expansion (DX) single-speed heating coil, which is a common element in electric HVAC systems.

Key functionalities of the measure include:

1. Coil Creation: Provides a utility to generate a single-speed DX heating coil with defined performance curves. This coil is critical in electric HVAC systems.
2. Performance Curves: Models the coil’s behavior across different conditions using cubic and biquadratic curves.

Given the measure’s name and visible content, it’s likely that the measure offers a broader range of functionalities related to converting non-electric HVAC components to their electric counterparts. However, to fully understand the measure’s capabilities, a deeper examination of its other methods, especially the run method, would be essential.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/deliverable/measures

set_water_heater_type_efficiency:

Purpose of the Measure:

This measure provides users with the ability to adjust the fuel type and thermal efficiency of water heaters in an OpenStudio model. By allowing users to select different fuel types and set specific efficiencies, the measure facilitates modeling scenarios where water heater performance and fuel source are crucial factors in the building’s energy profile.

For instance, transitioning from fossil fuel-based heaters to electric heaters is a common strategy in efforts to decarbonize buildings. Adjusting the thermal efficiency can also simulate scenarios with high-efficiency water heaters, leading to reduced energy consumption.

This measure provides a straightforward and efficient way to make these adjustments across all WaterHeaterMixed objects in a model, ensuring consistency and accuracy in simulations.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/tree/develop/deliverable/measures

AdjustBaseline_HVACCOP_Parallel:

Purpose of the Measure:

This script is designed to adjust the HVAC Coefficient of Performance (COP) in multiple EnergyPlus IDF files based on specific criteria found in a building dataset (provided as a CSV file). The script leverages parallel processing to expedite the adjustments across many IDF files simultaneously, making it efficient for large-scale building energy modeling tasks.

Key functionalities of the script include:

1. IDF File Processing: Adjusts properties of HVAC systems in IDF files based on building data.
2. Parallel Execution: Uses multiple CPU cores to process IDF files concurrently, leading to faster execution times.
3. HVAC Adjustments: Modifies the COP of HVAC components based on building-specific criteria, like the HVAC level and building area.

This script provides an automated and efficient way to make performance adjustments to HVAC systems across a large number of building energy models, ensuring consistency and accuracy in simulations.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/blob/develop/Projects/SmithGroup/SmithAdjustBaseline_HVACCOP_Parallel.py

AdjustRValues_Lighting_HVACCOP_Parallel:

Purpose of the Measure:

This script is designed to make adjustments to R-values (thermal resistance), lighting parameters, and HVAC Coefficient of Performance (COP) in multiple EnergyPlus IDF files. These adjustments are based on specific criteria found in a provided building dataset (CSV file).

The primary functionalities of the script include:

1. IDF File Processing: Modifies properties of walls, roofs, windows, and HVAC systems in IDF files based on building data.
2. Parallel Execution: Utilizes multiple CPU cores to process IDF files concurrently, leading to faster execution times.
3. Performance Adjustments: Tweaks R-values, lighting parameters, and HVAC COP values to reflect building-specific data.

This script provides an automated and efficient way to refine the performance attributes of building models across a large number of EnergyPlus IDF files. By using parallel processing, it ensures rapid adjustments, making it suitable for extensive building energy modeling tasks.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/blob/develop/Projects/SmithGroup/SmithAdjustRValues_Lighting_HVACCOP_Parallel.py

AdjustInfiltrationValuesEppy:

Purpose of the Measure:

This script is designed to adjust the infiltration values, specifically the effective air leakage area, in multiple EnergyPlus IDF files. The adjustments are based on specific criteria and involve halving the effective leakage area values, which results in a significant reduction in infiltration.

Key functionalities of the script include:

1. IDF File Processing: Adjusts the infiltration values (effective air leakage area) of buildings defined in the IDF files.
2. Parallel Execution: Uses multiple CPU cores to process IDF files concurrently, resulting in faster execution times.

This script provides an automated way to refine the infiltration attributes of building models across numerous EnergyPlus IDF files, aiming to influence the energy and comfort outcomes of building simulations.

Code Location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/blob/develop/Projects/CardinalGlass/AdjustInfiltrationValuesEppy.py

AdjustValuesEPPY_Parallel_WWR:

Purpose of the Measure:

This script is designed to adjust the Window-to-Wall Ratio (WWR) in multiple EnergyPlus IDF files using the geomeppy library. The purpose of adjusting WWR is to influence the energy performance and indoor environmental conditions of a building. A higher WWR might result in more daylighting but could also lead to increased heat gains or losses, affecting the building’s energy consumption.

The script emphasizes efficiency through parallel processing, which allows for faster adjustments by distributing the workload across multiple CPU cores.

Code location: https://code.ornl.gov/ORNL-BTRIC/NBEM/AutoBEM/-/blob/develop/Projects/CardinalGlass/AdjustValuesEPPY_Parallel_WWR.py

• Bass, Brett and New, Joshua R. (2022). “How Will United States Commercial Building Energy Use Be Impacted by IPCC Climate Scenarios?” Journal of Energy, volume 263(E), doi.org/10.1016/j.energy.2022.125945, Jan. 2023. [Energy] (free til 1/6/23)
• Bass, Brett, New, Joshua R., Clinton, Nicholas, Adams, Mark, Copeland, Bill, and Amoo, Charles (2022). “How close are urban scale building simulations to measured data? Examining bias derived from building metadata in urban building energy modeling.” Journal of Applied Energy, volume 327(1), doi.org/10.1016/j.apenergy.2022.120049, Dec. 1, 2022. [APEN] (free til 12/3/22)
• Bass, Brett, New, Joshua R., and Wade, Zachary (2022). “Future Typical Meteorological Year (fTMY) Weather Data and Climate Change Impacts to Maricopa County, Arizona.” The 2nd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities, Boston, Massachusetts, USA, Nov. 9-10, 2022. [PDF]
• Bass, Brett, New, Joshua R., Berres, Andy, Adams, Mark, Clinton, Nicholas, Leung, Marissa, Tuxen-Bettman, Karin, and Van Groenou, Saleem (2022). “Poster: Environmental Insights Explorer for Buildings.” Geo for Good Summit, Mountain View, A, Sept. 19, 2022. [PPT]
• Haowen Xu, Andy Berres, Yunli Shao, Chieh Wang and Joshua New (2022). “Towards a Smart Metaverse City: Immersive Realism and 3D Visualization of Digital Twin.” Advances in Scalable and Geospatial Analytics: New Trends, Challenges and Applications, CRC Press, Taylor & Francis Group.
• New, Joshua R., Bass, Brett, Berres, Andy, and Adams, Mark (2022). “Industry partners use ORNL software to trim carbon footprint of buildings” ORNL Science Highlight, Oak Ridge, TN, Sept. 20, 2022. [Article]
• Bass, Brett, New, Joshua, Amoo, Charles, Ezell, Evan, and Copeland, William (2021). “Using Measured Building Energy Data to Infer Building Type for Building Energy Modeling.” ASHRAE/IBPSA-USA 2022 Building Performance Analysis Conference and SimBuild (BPACS), Chicago, IL, Sept. 14-16, 2022. [PDF] [PPT]
• Berres, Andy, Bass, Brett, Adams, Mark, New, Joshua R. (2022). “AutoBEM: A Workflow to Automate Building Energy Modeling.” Computational and Autonomous Workflows Workshop (CAW 2022) Lightning Talk, Oak Ridge, TN, Sept. 12-13, 2022. [PPT]
• Bass, Brett, New, Joshua R., Rastogi, Deeksha, and Kao, Shih-Chieh (2022). “Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set].” Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [Data]
• Mumme, Sven, Brown, Fredericka, Bass, Brett, Shen, Bo, Shrestha, Som, New, Joshua R. and Gluesenkamp (2022). “Implications of Electrifying Residential Space Heating in Cold Climates with Heat Pumps, Envelope Improvements, and Thermal Storage.” ACEEE Summer Study Conference, Pacific Grove, CA, August 21-26, 2022. [OSTI] [PDF] [PPT]
• New, Joshua R. and Bass, Brett (2022). “Automatic Building Energy Modeling (AutoBEM) and its Model America dataset: background, capabilities, and discussion with WIP stakeholders.” DOE’s Weatherization and Intergovernmental Programs (WIP), July 28, 2022. [PPT]
• New, Joshua R. and Bass, Brett (2022). “Automatic Building Energy Modeling (AutoBEM) and the Model America dataset.” Digital Twin Demonstrator Forum, June 9, 2022.
• Amoo, Charles (2022). “Urban-Scale Building Energy Modeling: Market Potential, Data Sources, Computational Methods and Visualization.” PhD Qualifier Exams Presentation, May 16, 2022.
• Allen-Dumas, Melissa, Berres, Andy, Brelsford, Christa, New, Joshua R., Bass, Brett, Sweet-Breu, Levi, Kurte, Kuldeep, and Sanyal, Jibonananda (2022). “Sustainable Cities: Socioeconomics, Building Types, and Urban Morphology.” Smoky Mountain Computational Science Data Challenge (SMCD22), August 23-25, 2022. [Challenge]
• Bass, Brett, New, Joshua R., Curtis, Leland, McNally, Peter, and Sanborn, Stet (2022). “AI-facilitated building design.” DOE BTO Brown Bag, March 17, 2022. [PPT]
• Berres, Andy S., Sanyal, Jibonananda, Kurte, Kuldeep R., Dumas, Melissa R., Bass, Brett C., New, Joshua R., Im, Piljae, Urban, Marie L., and Thakur, Gautam (2022). “Traffic-based analyses of buildings advance smart city capabilities.” ORNL science article, March 3, 2022. [Article]
• Savage, Neil (2022). “Virtual Duplicates.” Communications of the ACM, volume 65(2), pp. 14-16, doi:10.1145/3503798, February 2022. [CACM]
• Webb, Sarah (2022). “Sustainable Cities: Oak Ridge researchers harness Argonne’s Theta supercomputer to build energy-efficiency models for all U.S. buildings.” ASCR Discovery, January 2022. [Article]
• Xu, Haowen, Berres, Andy, Yoginath, Srikanth, Kurte, Kuldeep, Peleti, Rajesh, New, Joshua R., and Sanyal, Jibonananda (2022). “Towards Adaptive Decision Support: A Perspective from Intelligent and Annotated Visual Analytics for Exploring Big Urban Mobility Data.” ASCR Workshop on Visualization for Scientific Discovery, Decision-Making, & Communication, Jan. 2022. [PDF]
• Bass, Brett and New, Joshua R. (2022). “AutoBEM – Dynamic Archetypes.” Generates representative models and floor space multipliers for any area of interest, with example data for Las Vegas, doi:10.5281/zenodo.5838465. January 11, 2022. [code]
• Berres, Andy, Bass, Brett, Adams, Mark, Garrison, Eric, and New, Joshua R. (2021). “A Data-Driven Approach to Nation-Scale Building Energy Modeling.” 2021 IEEE International Conference on Big Data, Orlando, FL, December 15-18, 2021. [PDF] [PPT]
• Bass, Brett, Curtis, Leland, and New, Joshua R. (2021). “Design Space Data: Informing Common Design Decisions with Pre-Simulated Data.” ORNL internal report ORNL/TM-2021/170499, November 19, 2021, 17 pages.
• New, Joshua R. (2021). “Model America – a model of every U.S. building.” CalBEM 2021 virtual conference, November 18-19, 2021. [PDF] [MOV]
• Bass, Brett, New, Joshua R., Ezell, Evan, Im, Piljae, Garrison, Eric, and Copeland, William (2021). “Utility-scale Building Type Assignment Using Smart Meter Data.” Building Simulation 2021 Conference, Bruges, Belgium, September 1-3, 2021. [PDF] [PPT] [MOV]
• New, Joshua R., Bass, Brett, Berres, Anne S. (2021). “Distribution of potential savings from urban-scale energy modeling of a utility.” Building Simulation 2021 Conference, Bruges, Belgium, September 1-3, 2021. [PDF] [PPT] [MOV]
• Berres, Anne S., Bass, Brett, New, Joshua R., Im, Piljae, Urban, Marie, and Sanyal, Jibonananda (2021). “Generating traffic-based building occupancy schedules in Chattanooga, Tennessee from a grid of traffic sensors.” Building Simulation 2021 Conference, Bruges, Belgium, September 1-3, 2021. [PDF] [PPT] [MOV]
• Allen-Dumas, Melissa, Brelsford, Christa, New, Joshua R., Berres, Anne, Kurte, Kuldeep, Sanyal, Jibonananda, Sweet, Levi (2021). “Sustainable Cities: Socioeconomics, Building Types, and Urban Morphology.” Smoky Mountain Computational Science Data Challenge (SMCD21). Virtual Conference, August 24-August 26, 2021. [Challenge]
• Bass, Brett, New, Joshua R., Adams, Mark, and Berres, Andy (2021). “Model America – WRF Grid Cell Archetype extract from ORNL’s AutoBEM (1.0) [Data set].” Zenodo, doi.org/10.5281/zenodo.5154216, August 2, 2021. [DOI]
• New, Joshua R. (2021). “Nation-scale building energy modeling, climate change, and potential grid impacts.” IEEE Power & Energy Society General Meeting (PES GM) panel session titled “Emerging applications of data-driven intelligence as an enabler for demand response in wholesale and local markets.” Virtual Conference, July 28, 2021. [PPT]
• Data: New, Joshua R., Bass, Brett, Adams, Mark, Berres, Anne, and Luo, Xuan (2021). “Los Angeles County Archetypes in Weather Research and Forecasting (WRF) Region from ORNL’s AutoBEM [Data set].” Zenodo, doi.org/10.5281/zenodo.4726136, Apr. 28, 2021. [Zenodo]
• Data: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). “Model America – data and models of every U.S. building.” ORNL Constellation, https://doi.ccs.ornl.gov/ui/doi/339, April 14, 2021.
• Data: New, Joshua R., Bass, Brett, Adams, Mark, and Berres, Anne (2021). “Clark County (Vegas) Archetypes from ORNL’s AutoBEM [Data set].” Zenodo, doi.org/10.5281/zenodo.4552901, Mar. 21, 2021. [Zenodo]
• Data: New, Joshua R., Bass, Brett, Adams, Mark, and Berres, Anne (2021). “Model America – Clark County (Vegas) extract from ORNL’s AutoBEM (Version 1.1) [Data set].” Zenodo, doi.org/10.5281/zenodo.4552901, Feb. 16, 2021. [Zenodo]
• Wang, Jing, Ye, Yunyang, Zuo, Wangda, New, Joshua R., and Rose, Amy (2021). “City-scale Building Occupancy Prediction using Geographic Information System Data.” engrXiv journal, doi.org/10.31224/osf.io/658yb, Feb. 9, 2021. [engrXiv] [PDF]
• Garrison, Eric and New, Joshua R. (2021). “Quality Control Methods for Advanced Metering Infrastructure Data.” Smart Cities journal, Special Issue “Applied Artificial Intelligence in Energy Systems,” volume 4(1), pages 195-203, doi.org/10.3390/smartcities4010012, Jan. 28, 2021. [SmartCities] [PDF]
• Bass, Brett, New, Joshua R., and Copeland, William (2020). “Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area.” Energies journal, Special Issue “Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings,” volume 14(1), issue 132, doi.org/10.3390/en14010132, Dec. 29, 2020. [Energies] [PDF] [OSTI]
• Allen-Dumas, Melissa R., Rose, Amy N., New, Joshua R., Omitaomu, Olufemi A., Yuan, Jiangye, Branstetter, Marcia L., Sylvester, Linda M., Seals, Matthew B., Carvalhaes, Thomaz M., Adams, Mark B., Bhandari, Mahabir S., Shrestha, Som S., Sanyal, Jibonananda, Berres, Anne S., Kolosna, Carl P., Fu, Katherine S., and Kahl, Alexander C. (2020). “Impacts of the Morphology of New Neighborhoods on Microclimate and Building Energy Use.” Renewable & Sustainable Energy Reviews, volume 133, 110030, ISSN 1364-0321, doi.org/10.1016/j.rser.2020.110030, November 2020. [RSER] [PDF]
• New, Joshua R., Adams, Mark, Garrison, Eric, Bass, Brett and Guo, Tianjing. (2020). “Scaling Beyond Tax Assessor Data.” ASHRAE/IBPSA-USA 2020 Building Performance Analysis Conference & SimBuild (BPACS), Chicago, IL, Sept. 29 – Oct. 1, 2020. [PDF] [PPT] [MP4]
• Bass, Brett and New, Joshua R. (2020). “Future Meteorological Year weather data from IPCC Scenarios.” ASHRAE/IBPSA-USA 2020 Building Performance Analysis Conference & SimBuild (BPACS), Chicago, IL, Sept. 29 – Oct. 1, 2020. [PDF] [PPT] [MP4]
• Presentation: New, Joshua R. (2020). “Digital Twin of a Utility: Beyond Urban-Scale Building Energy Modeling.” Invited Speaker to ORNL’s monthly Energy Talks, Oak Ridge, TN, July 11, 2020. [PPT]
• Bass, Brett and New, Joshua R. (2019). “Potential Demand Reduction from Buildings in a Simulated Utility.” Invited speaker to the ACM BuildSys conference UrbSys workshop, Columbia University New York City, NY, November 10, 2019. [ACM] [PDF] [PPT]
• Presentation: Hong, Tianzhen, Jain, Rishee, New, Joshua R., Reinhart, Christoph, Polly, Ben, Luo, Xuan (2019). “Panel – Urban Information and Energy Modeling.” Proceedings of the IBPSA Building Simulation Conference, Rome, Italy, Sept. 2-4, 2019. [PPT]
• New, Joshua R., Adams, Mark, Garrison, Eric, Copeland, William, Smith, Brian, and Campbell, Andy (2019). “Nailing the Peak: City-Scale, Building-Specific Load Factor and Contribution to a Utility’s Hour of Critical Generation.” Proceedings of the IBPSA Building Simulation Conference, Rome, Italy, Sept. 2-4, 2019. [PDF] [PPT]
• New, Joshua R. (2019). “Automatic Building detection and Energy Model creation (AutoBEM) technologies for remote audit of individual buildings at urban scales.” Invited speaker to the EnergyExchange, presented as part of a seminar titled “Energy and Water Assessments: Virtual and Conventional”, Denver, CO, August 20-22, 2019. [PDF]
• Hussein, Ahmed, Eicker, Ursula, and New, Joshua R. (2019). “A Comparison Between Two Urban-Scale Methods for The Assessment of Heat Energy Demand and Photovoltaic Potential in New York City, USA.” Proceedings of the European International Conference on Transforming Urban Systems (EICTUS), University of Strasbourg, France, June 26-28, 2019. [PPT]
• New, Joshua, Copeland, William, and Ingraham, James (2018). “Poster: Virtual Electric Power Board of Chattanooga, TN (EPB).” ORNL BTO Peer Review, April 15, 2019. [Poster]
• Garrison, Eric, New, Joshua R., and Adams, Mark (2019). “Accuracy of a Crude Approach to Urban Multi-Scale Building Energy Models Compared to 15-min Electricity Use.” Best PhD Student Paper award. In Proceedings of the ASHRAE Winter Conference, Atlanta, GA, Jan. 12-16, 2019. [PDF] [PPT]
• Luo, Xuan, Macumber, Dan, New, Joshua R., and Judkoff, Ron (Seminar Chair) (2018). “Seminar – Multiscale Building Energy Modeling, Part 10.” In Proceedings of the ASHRAE Winter Conference, Atlanta, GA, Jan. 12-16, 2019. [LBNL] [NREL] [ORNL]
• New, Joshua R. (2018). “TEDergy Talk: Automatic Building Energy Modeling (AutoBEM).” In Proceedings of the Building Performance Analysis Conference and SimBuild (BPACS) co-organized by ASHRAE and IBPSA-USA, Chicago, IL, Sept. 26-28, 2018. [PDF] [PPT]
• New, Joshua R., Garrett, Aaron, Sanyal, Jibonananda, Slattery, Bob, Gehl, Anthony, and Miller, William A. (2019) “Big Data Mining for Assessing Calibration of Building Energy Models.” In International Journal of Computer & Software Engineering (IJCSE), volume 3, issue 136, Sept. 8, 2018. [PDF]
• New, Joshua R., Adams, Mark, Im, Piljae, Yang, Hsiuhan, Hambrick, Joshua, Copeland, William, Bruce, Lilian, Ingraham, James A. (2018). “Automatic Building Energy Model Creation (AutoBEM) for Urban-Scale Energy Modeling and Assessment of Value Propositions for Electric Utilities.” In Proceedings of the International Conference on Energy Engineering and Smart Grids (ESG), Fitzwilliam College, University of Cambridge, Cambridge city, United Kingdom, June 25-26, 2018. [PDF] [PPT]
• Ingraham, James A. and New, Joshua R. (2018). “Virtual EPB.” Presented to Building Technologies Office following the BTO Peer Review, 87 slides. Arlington, VA, May 3, 2018. [PPT]
• New, Joshua R., Omitaomu, Olufemi, Yuan, Jiangye, Yang, Hsiuhan (Lexie), Carvalhaes, Thomaz, Sylvester, Linda, and Adams, Mark (2017). “AutoBEM: Automatic Detection and Creation of Individual Building Energy Models for Each Building in an Area of Interest.” In Proceedings of the 2nd International Energy and Environment Summit, Dubai, UAE, November 18-20, 2017. [PDF] [PPT]
• New, Joshua R. (2017). “ORNL projects related to the Urban Dynamics Institute (UDI) and Automatic Building Energy Model creation (AutoBEM).” Presented as part of a multi-lab workshop at the Smart Cities Week, Washington, D.C., October 3, 2017. [PPT]
• New, Joshua R., Chen, Yixing, Choi, Joon-Ho, and Bass Abushakra (Seminar Chair) (2017). “Seminar 28 – Urban-Scale Energy Modeling, Part 5” presenting “Automatic Building Energy Model Creation (AutoBEM).” In Proceedings of the ASHRAE Annual Conference, Long Beach, CA, June 26, 2017. [ORNL] [LBNL] [USC]
• New, Joshua R., Adams, Mark, Bhandari, Mahabir, Shrestha, Som, and Sanyal Jibonananda (2017). “Auto-generated Building Energy Models (AutoBEM) of Urban Morphologies and Analysis of Microclimate Interaction.” Presented to the Urban Dynamics Institute Scientific Advisory Board, March 23, 2017. [Poster]