Buildings in cities consume 30 to 70% of the cities' total primary energy. Retrofitting the existing building stock to improve energy efficiency and reduce energy use is a key strategy for cities to reduce green-house-gas emissions and mitigate climate change. Planning and evaluating retrofit strategies for buildings requires a deep understanding of the physical characteristics, operating patterns, and energy use of the building stock. This is a challenge for city managers as data and tools are limited and disparate.

City Building Energy Saver (CityBES) is a web-based data and computing platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale efficiency programs. CityBES uses an international open data standard, CityGML, to represent and exchange 3D city models. CityBES employs EnergyPlus to simulate building energy use and savings from energy efficient retrofits. CityBES provides a suite of features for urban planners, city energy managers, building owners, utilities, energy consultants and researchers.

The following figure shows the three layers of the software architecture of CityBES: the Data layer, the Algorithms and Software layer, and the Use Cases layer. The Data layer includes the weather data, and the 3D city model (CityGML or GeoJSON). The Software layer includes EnergyPlus, OpenStudio and CityBES. The Use Cases layer provides examples of potential applications, including energy benchmarking, energy retrofit analysis, renewable energy analysis, building performance visualization, as well as climate data analysis and visualization.

Y. Chen, Z. Deng, X. Guo, T. Hong. Automatic and Rapid Calibration of Urban Building Energy Models by Learning from Energy Performance Database. Applied Energy, 2020.
T. Hong, M. Ferrando, X. Luo, F. Causone. Modeling and Analysis of Heat Emissions from Buildings to Ambient Air. Applied Energy, 2020.
M. Ferrando, F. Causone, T. Hong, Y. Chen. Urban Building Energy Modeling (UBEM) Tools: A State-of-the-Art Review of bottom-up physics-based approaches. Sustainable Cities and Society, 2020.  
X. Luo, T. Hong, Y.H. Tang. Modeling thermal interactions between buildings in an urban context. Energies, 2020.
K. Sun, M. Specian, T. Hong. Nexus of Thermal Resilience and Energy Efficiency in Buildings: A case study of a nursing home. Building and Environment, 2020.
A.T.D. Perera, V.M. Nik, D. Chen, J.L. Scartezzini, T. Hong. Quantifying the impacts of climate change and extreme climate events on energy systems. Nature Energy, 2020.
T. Hong, Y. Chen, X. Luo, N. Luo, S.H. Lee. Ten questions on urban building energy modeling. Building and Environment, 2020.
Y. Chen, T. Hong, X. Luo, B. Hooper. Development of City Buildings Dataset for Urban Building Energy Modeling. Energy and Buildings, 2018.
R. Jain, X. Luo, G. Sever, T. Hong, C. Catlett. Representation and evolution of urban weather boundary conditions in Downtown Chicago. Building Performance Simulation, 2018.
T. Hong, Y. Chen, M.A. Piette, X. Luo. Modeling City Building Stock for Large Scale Energy Efficiency Improvement using CityBES. ACEEE Summer Study in Building Energy Efficiency, 2018.
X. Luo, T. Hong. Modeling building energy performance in urban context. ASHRAE BPAC / SimBuild Conference, Chicago, 2018.
Y. Chen, T. Hong. Impacts of Building Geometry Modeling Methods on the Simulation Results of Urban Building Energy Models. Applied Energy, 2018.
Y. Chen, T. Hong, M.A. Piette (2017). Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis. Applied Energy, 2017.
T. Hong, Y. Chen, S.H. Lee, M.A. Piette. CityBES: A Web-based Platform to Support City-Scale Building Energy Efficiency. Urban Computing, 2016.
Y. Chen, T. Hong, M.A. Piette. City-Scale Building Retrofit Analysis: A Case Study using CityBES. Building Simulation, 2017.

CityBES is sponsored by Lawrence Berkeley National Lab, under the Laboratory Directed Research and Development (LDRD) Program.

This website and related content were prepared as an account of or to expedite work sponsored at least in part by the United States Government. While we strive to provide correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.

Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. Use of the Laboratory or University’s name for endorsements is prohibited.

The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or The Regents of the University of California. Neither Berkeley Lab nor its employees are agents of the US Government.

Berkeley Lab web pages link to many other websites. Such links do not constitute an endorsement of the content or company and we are not responsible for the content of such links.

More information is available here.

Please start an analysis by selecting a shared building dataset

Select a city
Select a district

Select a district

Select a district

Select a district

Select a district

Select a district

Select a district

Select a district

Select a district

Select a district

Please contact the development team if you would like to upload and manage your own building datasets. Thank you!

Parameter	Options
Result options
Color buildings by
Color buildings by
Color buildings by
Color buildings by
Color buildings by
Show summary by

Building editing is disabled for this project.

Note: the editing is not auto-saved. Please click the Save... button in each section to save the editing.

Edit Building Basic Information

Building type
Year Built
Number of Stories
Building height (m)

Success! Basic information updated.

Edit System Configuration Parameters

Internal Loads

Lighting Power Density (W/m²)
Equipment Power Density (W/m²)
Occupancy Density (person/m²)
Outdoor Air per Person (m³/s.person)
Infiltration Calculation Method
Infiltration Rate per Exterior Wall Area (m³/s.m²)
Air Change per Hour

Envelope

Roof Insulation R-value (F.ft².hr/Btu)
Roof Solar Reflectance
External Wall Insulation R-value (F.ft².hr/Btu)
Window U-factor (Btu/F.ft².hr)
Window Solar Heat Gain Coefficient (SHGC)
Window Visual Transmittance (VT)
Window-to-wall Ratio (WWR)

Service hot water system

Water heater type
Water heater efficiency
Heat pump water heater COP

HVAC System

HVAC System Type
Cooling COP
Heating Efficiency
Fan Efficiency
Air Economizer Type

Operational Schedule and Setpoints

Operational schedule
Heating setpoint (°F)
Cooling setpoint (°F)

Success! System Configuration Parameters updated.

After the building information is updated, click the Update Results button to update the baseline simulation, retrofit analysis and photovoltaics potential analysis.

Benchmarking with DOE Building Performance Database

CityBES Current Building Filters

Properties

Options / Range

Building Type

Small Office (<=3 Floors and <2322 m²)

Medium Office (<=5 Floors and 2322 to 9290 m²)*

Large Office (>=6 Floors or >9290 m²)

Small Retail (<=2 Floors and <1200 m²)

Medium Retail (<=2 Floors and 1200 to 4645 m²)

Full Service Restaurant (<=2 Floors and >=350 m²)

Large Hotel (>=4 Floors and >=6000 m²)

Single Family House

Multi Family House

Year Built

Total Floor Area

Note(*): Medium Office buildings also include 4 or 5 Floors and <2322 m²

Benchmarking with DOE Building Performance Database

Select a City to Compare With

Benchmarking results are out of date due to change of building filters. Please update the results.

ID	ECM Category	ECM Name
ECM

Special Permission is required to edit the ECM cost and efficiency data for global projects.

The updated cost and efficiency values only apply to new ECM packages. It does not impact the ECM Packages that are currently running or already finished.

ECM Cost

Capacity	Cost per unit

Cost per unit

ECM Embodied Greenhouse Gas (GHG) Emission

Embodied GHG emission per unit ()
Product service lifetime (year)
Note: Building life cycle stages and modules in the GHG embodied emission includes only the production (cradle to gate) stages (A1~A3) based on CEN 15978.

ECM Efficiency Values

For

Parameter

Value

Editing schedule is not available yet.

Special Permission is required to edit the incentives data for global projects.

The updated incentive data only apply to new ECM packages. It does not impact the ECM Packages that are currently running or already finished.

For detailed Incentives/Rebates information, please visit Database of State Incentives for Renewables & Efficiency.

Are there any Incentives/Rebates?

true

What kinds of Incentives/Rebates?

	Type 1: Rebate Based on Savings
	Type 2: Rebate Based on Application
	Type 3: Rebate Based on Program Participation or Tax Exemption
	Type 4: Rebate Based on Interest Savings

Type 1: Rebate Based on Savings

ID	Incentive Type	Incentive Rate	Incentive Unit	Maximum	Delete

Type 2: Rebate Based on Application

ID	Equipment Type	Incentive Rate	Incentive Unit	Maximum	Delete

Type 3: Rebate Based on Program Participation or Tax Exemption

ID	Incentive Program Name	Rebate or Tax Exemption $ Amount (One time or per year)	Delete

Type 4: Rebate Based on Interest Savings

ID	Loan $ Amount	Current AIR_* (%)	Program AIR_* (%)	Loan Years	Loan $ Savings	Delete

* AIR: Annual Interest Rate

Special Permission is required to edit the incentives and ECMs connection for global projects.

Type 1: Rebate Based on Savings

Type 2: Rebate Based on Application

ID	ECM Category	ECM Name
ECM

ID	ECM Category	ECM Name
ECM

This feature estimate the energy generation of the photovoltaic (PV) energy systems.

Please specify the area for PV and modify the parameters of a PV module in the following panels.

Parameters of a PV module (Available from manufacturer's specifications)

Cell Type
Number of cells in a module
Current at maximum power (A)
Voltage at maximum power (V)
Short circuit current (A)
Open circuit voltage (V)
Area of the PV module (m²)

Fig. Illustration of a PV system: Cell=>Module=>Array

Area for PV

Percentage of roof area for PV (%)
Tilt angle from horizontal (degree)
Orientation

Click the Calculate Photovoltaic Potential button below to start the simulation.

Progress

District Heating and Cooling System

Introduction

This feature imports and visualizes a cooling and heating load profile of a district of buildings, then users select a few district energy system types and specify their characteristics for evaluation, next EnergyPlus models are created and simulations are run to calculate the energy use and energy cost of the selected district energy systems, and finally simulation results are shown for users to compare performance between the selected district energy systems.

A load profile is a csv file with 8760 hourly values of the district’s heating demand (W), cooling demand (W), and the electricity consumption (kWh) and natural gas consumption (MMBTU) associated with the cooling and heating demands.

Currently, five types of district energy systems are supported, including water-cooled chillers and boilers, water-cooled chillers with ice-storage and boilers, heat-recovery chillers and heat pumps, and geothermal heat pump. Future system types will include CHP.

Load Profile

Describe Systems and Simulate

Please upload heating and cooling load profiles first.

Results

No simulation results yet.

The feature is a result of a research collaboration between Norwegian University of Science and technology (NTNU), Norway (Aoife Houlihan Wiberg) and LBNL, US (Yixing Chen, Tianzhen Hong). Aoife Houlihan Wiberg was a Visiting Scholar at LBNL from 2017 to 2018. The work involves the further development of the CityBES tool to include the addition of an embodied carbon parameter to selected ECM Retrofit measures, the results of which can be scaled up and visualised in the CityBES tool.

An attributional, process based life cycle assessment is applied (EC, 2010). The life cycle boundary includes the production stages (A1~A3) and operational stages as defined in the ZEB OM Balance (Fufa et al., 2016). The construction process stage and end of life stages are omitted. In many previous life cycle assessments of buildings (Dahlstrom et al., 2012, Ghose, 2012, Cabeza et al., 2014, John 2013) , the construction and end of life stages were found not to have been significant as the product and use stage. The functional unit is one square metre of heated floor area over a reference study period of 60 years (Hestnes and Eik~Nes, 2017, NS 3940 2012, 2012). Embodied and operational emissions are quantified using the indicator for global warming potential (GWP), and the emissions of GHG are measured in CO2 equivalents with the 100 year perspective (IPCC, 2013). The basis for the background data for the Life Cycle Assessment Data used in the CityBES ECM retrofit measures is KBOB Life Cycle Assessment Data 2016, which is based on Ecoinvent data version 2.2 (http://www.lc-inventories.ch/, https://www.ecoinvent.org/).

Parameters of Life Cycle Greenhouse Gas (GHG) Emission

Reference Study Period (years)

Acknowledgements

The developers gratefully acknowledge the support from the Research Council of Norway and the Research Center on Zero Emission Buildings (ZEB) and The Research Centre for Zero Emission Neighbourhoods in Smart Cities (ZEN) hosted by Norwegian University of Science and technology (NTNU), Trondheim, Norway.

Relevant Publications

Kristjansdottir, Torhildur Fjola; Houlihan Wiberg, Aoife Anne Marie; Andresen, Inger; Georges, Laurent; Heeren, Niko; Good, Clara; Brattebø, Helge. (2018) Is a net life cycle balance for energy and materials achievable for a zero emission single-family building in Norway?. Energy and Buildings.
Nygaard Rasmussen, Freja; Malmqvist, Tove; moncaster, alice; Houlihan Wiberg, Aoife Anne Marie; Birgisdottir, Harpa. (2017) Analysing methodological choices in calculations of embodied energy and GHG emissions from buildings. Energy and Buildings. vol. 158 (1487).
Birgisdottir, Harpa; moncaster, alice; Houlihan Wiberg, Aoife Anne Marie; Chae, Chang-U; Yokoyama, Keizo; Balouktsi, Maria; Seo, Seongwon; Oka, Tatsuo; Lutzkendorf, Thomas; Malmqvist, Tove. (2017) IEA EBC Annex 57 ‘Evaluation of Embodied Energy and CO2eq for Building Construction’.Energy and Buildings. vol. 154.
Georges, Laurent; Haase, Matthias; Houlihan Wiberg, Aoife Anne Marie; Kristjansdottir, Torhildur; Risholt, Birgit Dagrun. (2015) Life cycle emissions analysis of two nZEB concepts. Building Research & Information. vol. 43 (1).
Lutzkendorf, Thomas; Foliente, Greg; Balouktsi, Maria; Houlihan Wiberg, Aoife Anne Marie. (2015) Net-zero buildings: incorporating embodied impacts.Building Research & Information. vol. 43 (1).
Houlihan Wiberg, Aoife Anne Marie; Georges, Laurent; Dokka, Tor Helge; Haase, Matthias; Time, Berit; Lien, Anne Gunnarshaug; Mellegård, Sofie Elisabet; Maltha, Mette Maren. (2014) A net zero emission concept analysis of a single-family house. Energy and Buildings. vol. 74 (May).

Assign a utility rate to each building type

Building Type	Utility Rate Assignment
Small Office
Medium Office
Large Office
Small Retail
Medium Retail
Full Service Restaurant
Large Hotel
Single Family House
Multi Family House

Click the "Save" button to update the information after editing.

Select a Utility Rate:

Electricity

Season	Date	Type	Time
Summer		Summer peak	to
	to	Summer mid-peak	to , to
		Summer off-peak	00:00 to , to 24:00
Winter	1-1 to , to 12-31	Winter peak	to
		Winter mid-peak	to , to
		Winter off-peak	00:00 to , to 24:00
Electricity monthly charge ($/month):
Electricity demand charge ($/kW)

Natural Gas

Season	Type	Date
Summer		to
Winter	Winter peak	to
Winter	Winter off-peak	to , to
Natural gas monthly charge ($/month)

Electricity usage charge rate ($/kWh)
Natural gas usage charge rate ($/therm)
Monthly electricity demand charge ($/kW)

Utility rate successfully updated!

Note: When the simulation is done, you can look at the results by color coding the buildings at the 'District Buildings' tab.

Running large-scale simulation requires significant computing resource. Please contact the development team to access this feature.

Zoning Method
For Tall Buildings (>=4 stories)	Use Floor Multiplier
For Surrounding Buildings	Model as Shading Buildings
Create Load Profiles for Baseline Simulation	Yes
Create Load Profiles for Retrofit Scenarios	Yes
Re-run Baseline Models?
Re-run Retrofit Models?
Electricity usage charge rate ($/kWh)
Natural gas usage charge rate ($/therm)
Monthly electricity demand charge ($/kW)
Generate .idf files without running simulation	Yes

CityBES includes a library of 877 TMY3 weather files for cities in U.S. By default, CityBES assigns the closest weather file for each building based on their GIS location. When there are buildings covering large area, multiple weather files may be used to consider the micro-climate conditions.

CityBES allows users to use a customized weather file for all the buildings in the dataset. In this case, the customized weather file will be applied to all the buildings in the dataset.

Use custom EnergyPlus weather file

The Uploaded weather file will be used in the analysis.
If you would like to upload a new weather file, please choose the new EPW file and click the Upload selected EPW file button.
The file should have EPW format.

For Baseline Only

For Baseline and Retrofit Scenarios

For Resilience Scenario

Prepare the hourly results for the resilience simulation. Only the results of filtered buildings during the period of extreme event will be prepared, for both the resilience scenario and the normal scenario simulated by the user.

For District System

Prepare the aggregated load profile for the filtered buildings only. A zip file will be downloaded containing one csv file for load file and one csv file for the information of buildings included.

Note: The hourly load profiles include the facility level electricity, natural gas, and water consumption for each building. It also included the plant loop heating and cooling demand to support the analysis of district energy systems.
The electricity consumption data are further broken down to end use level for cooling, heating, interior lighting, interior equipment, exterior lighting, exterior equipment, fans, pumps, heat rejection, humidification, heat recovery, water systems, refrigeration, and generators.
The gas consumption data are split into water system heating and space heating.
The water consumption data are split into the water system use and the heat rejection via cooling tower.
The plant loop heating demand data are split into water systems and HVAC systems. The plant loop cooling demand only contain the facility level data.

The City Building Energy Saver (CityBES) is developed by a team led by Dr. Tianzhen Hong in Building Technology and Urban Systems Division at LBNL.

Team Members at LBNL

Tianzhen Hong, (PI, thong@lbl.gov, 510-486-7082)

Wanni Zhang (Developer, wannizhang@lbl.gov)

Han Li (Developer, District Energy System)

Xuan Luo (Developer)

Kaiyu Sun (Developer, CBES)

Mary Ann Piette (Senior Adviser)

Yixing Chen, Ph.D. (Former LBNL Developer)

Yujie Xu (Former Student Intern, Climate visualization)

Daryn Lee (Former Student Intern, Dataset Development)

Bill Zhai (Student Assistant, Dataset support)

Please fill in the Google form below for permission request.	Please fill in the Google form below for technical support.
Loading...	Loading...

Notes for Building Dataset Management

The code list (case insensitive) for building types are as follows. The size of the building is calculated based on the building footprint and the number of stories. All other codes are currently treated as "Others" building type, but the original labels in your dataset will be recorded in our database. Once we can handle those building types in CityBES, the data can be processed again to further assign the building type for those buildings.

Office: office, 211, 212, 500, b, bz, o, oa, oah, oal, oam, oat, obh, obl, obm, oc, och, ocl, ocm, omd, oz
Retail: retail, 202, 206, 208, 209, c, c1, cz, rh1, s,
Restaurant: restaurant, 201
Hotel: hotel, h, h1, h2, hc, m, 110
Single Family House: 101, d, cos, dbm, th, thbm, single family, single-family, residential-single family, single-family building, single family building, single-family house, single family house
Multi Family House: 102, 104, 103, z, ZBM, ZEU, da, da15, da5, a, a15, a5, co, f, f15, f5, ti15, tia, tic, tic5, tif, multi family, multi-family, residential-multi family, multi-family building, multi family building, multi-family house, multi family house

For Coordinate Reference System (CRS) or Spatial Reference System (SRS), please convert your dataset to use one of the following CRS/SRS system. You can use QGIS (http://www.qgis.org), a free and open source geographic information system tool, to do the conversion.

EPSG:2227 for San Francisco, CA
EPSG:2229 for Los Angeles, CA
EPSG:32118 for New York City, NY
EPSG:2992 for Portland, OR
EPSG:2253 for Chicago, IL

The Unique index is used to sort (smallest to largest) the Building Dataset list in the Start tab.
The Unique building dataset name is used in the Building Dataset list in the Start tab.
The User-defined building id filed is for user to identify the buildings they upload in the results downloaded. It has to be unique in each dataset.

The Minimum longitude, Maximum longitude, Minimum latitude, and Maximum latitude are used to create a boundary. Only the buildings inside the boundary are added to the dataset. This is optional.

Batch upload building datasets

To add multiple building datasets at once, compress all your data model files into a zip file. Data model files in GeoJSON format or CityGML format can be zipped in one file, and they will be identified by the filename extension. Prepare another json file for all the information need as inputs for each dataset. Each object in the json file should have exactly the same name as the data model file (including the filename extension). Each object should contain the following key/value pairs.

Key	Requirement	Data Type
index	required	Number (Integer)
city	required	String
name	required	String
building_type_filed	required for GeoJSON file	String
measured_height_filed	required for GeoJSON file	String
year_built_filed	required for GeoJSON file	String
number_of_stories_filed	required for GeoJSON file	String
building_name_filed	optional	String
user_defined_building_id_filed	optional	String

Optional key/value pairs can be omitted in the json file if not applicable.

Select a city:

Exposure
Sensitivity
Adaptation
Overall HVI

Exposure

Overheat Days

Longest Overheat-day Streak

High HI Hours

PM2.5 Concentration

Ozone Exceedance

Introduction

Extreme heat is one of the leading causes of weather-related deaths. It leads to an average of 658 deaths per year (CDC, 2017). Climate change could make heat wave more frequent, more severe, and longer lasting, causing more deaths (IPCC 2007). By late-century (2071–2100), the average temperature of continental US could increase by approximately 5.0°F (2.8°C) for RCP4.5 and 8.7°F (4.8°C) for RCP8.5, relative to 1976–2005. The figure on the right is from (Hsiang et al., 2017). It projected as high as 80% increase in all-cause deaths by the end of the century.

A heat vulnerability index map could highlight sub-groups / regions susceptible to heat induced damages, so that necessary interventions or infrastructure could be prepared ahead of time. It could also assist the planning of emergency responses to improve the resilience to extreme heat events.

To evaluate the vulnerability to extreme heat, three separate HVI sub-indices are constructed: exposure, sensitivity, and adaptation. Exposure reflects the severity of the problem. Currently it includes five outdoor exposure factors: two temperature derived heatwave characteristics, heat index, and two air quality variables. An indoor building heat resistance indicator that characterizes the indoor heat level will be added in the next version. Sensitivity factors identify sub-groups who are more susceptible to severe damages than general public under similar heat exposure. Elderly or children, education, race, poverty, and pre-existing condition fall into this category. The third factor is adaptation, including factors that modifies the response to extreme heat. Income and the availability of green space are in this category.

The overall HVI is an aggregation of the three sub-indices: Exposure * Sensitivity / Adaptation

Data source

The following table shows the data sources.

Factor	Variable	Definition	Source
Exposure	Overheat days	Number of overheat days (daily maximum temperature above 30°C, and daily minimum temperature above 22°C)	Derived from hourly temperature from weather underground (WeatherHistory & Data Archive, 2020)
	Longest overheat-day streak	Number of consecutive overheat days
	Hours with dangerous Heat Index (HI)	Number of hours with heat index in range of "danger" or "extreme danger"	Derived with hourly temperature, and RH, from weather underground (WeatherHistory & Data Archive, 2020)
	pm2.5 concentration	Annual mean concentration in ug/m3	California Heat Assessment Tool (Four Twenty Seven, 2018)
	Ozone exceedance	Ozone exceeding state standard	California Heat Assessment Tool (Four Twenty Seven, 2018)
	Building heat resistance indicator		Developed for the project using building simulation data
Sensitivity	percent elderly	percent of populaiton over 65	American Community Survey - 5-year estimate, 2013 – 2017 (U.S. Census Bureau, 2017)
	percent children	percent of population under 5
	percent non-white	Percent of non-white population
	percent poverty	Percent of population below poverty level threshold
	percent low-education	percent of population without a high school degree
	percent with cognitive disability	Percent of population with “serious difficulty concentrating, remembering, or making decisions.” (U.S. Census Bureau, 2018)
	percent with ambulatory disability	Percent of population with “serious difficulty walking or climbing stairs.” (U.S. Census Bureau, 2018)
	Asthma prevalence	Asthma hospitalization rate per 10,000 people	California Heat Assessment Tool (Four Twenty Seven, 2018)
	Heart attack prevalence	Heart attack rate per 1,000 people	California Heat Assessment Tool (Four Twenty Seven, 2018)
Adaptation	median income		American Community Survey - 5-year estimate, 2013 – 2017 (U.S. Census Bureau, 2017)
Adaptation	parks	Percent of the census tract area covered in parks	City of Fresno GIS Data Hub (City of Fresno, 2020)

Interface

The HVI web map consists of three separate HVI sub-index views. The three sub-index views can be toggled using Component 1 in the following diagram. In each sub-index map view, the corresponding HVI sub-index is shown on the left (Component 2), and a various number of factor map views are shown on the right (Component 4). Each factor map view shows an ordinal of 1-5, indicating the severity of the corresponding variable. The HVI sub-index is produced with a weighted average of the factor views for each census tract. The weights of each factor could be adjusted with Component 6. User will need to enter a non-negative value in Component 6. The values will be normalized to produce the weights.

References

CDC. (2017). Heat-related illness. https://www.cdc.gov/pictureofamerica/pdfs/Picture_of_America_Heat-Related_Illness.pdf
City of Fresno. (2020). City of Fresno Data Hub. https://gis-cityoffresno.hub.arcgis.com/
Four Twenty Seven. (2018). The California Heat Assessment Tool: Planning for the Health Impacts of Extreme Heat. http://427mt.com/wp-content/uploads/2018/08/427-CHAT-report.pdf
Hsiang, S., Kopp, R., Jina, A., Rising, J., Delgado, M., Mohan, S., Rasmussen, D. J., Muir-Wood, R., Wilson, P., Oppenheimer, M., Larsen, K., & Houser, T. (2017). Estimating economic damage from climate change in the United States. Science, 356(6345), 1362–1369. https://doi.org/10.1126/science.aal4369
U.S. Census Bureau. (2017). 2013-2017 American Community Survey 5-Year Estimates.
U.S. Census Bureau. (2018). American Community Survey and Puerto Rico Community Survey 2018 Subject Definitions.
Weather History & Data Archive. (2020, August). Weather Underground. https://www.wunderground.com/history

This feature allows users to select extreme weather events from historical or future weather records, and evaluate scenarios of resilience of buildings from the perspective of providing comfort and services to occupants. Users can simulate and evaluate how technologies and operation strategies can improve occupant resilience to prioritize investment or inform decision making.

Select an Extreme Scenario

Extreme Event Settings

Lasting Period

Start time

End time

Building Operation Conditions

Select conditions related to the building operation that are applicable to the extreme event.

Complete power off

Only HVAC power off

Gas off

Shelter in place

Weather Conditions

Uploaded weather file:

Simulate normal scenarios (with the same weather file)

Simulation Results

Select the simulation results for resilience or normal cases to visualize. If multiple sets of results are selected, the median values of all buildings are shown. Buildings to be shown can be filtered on the District Buildings tab.

Uploaded weather file:
Uploaded weather file:	No file uploaded yet
Upload a new weather file:

Zip file for all data model files (required)
Json file for information of all datasets (required)

Overview

Publications

Acknowledgment

Disclaimers

Welcome, SF Environment

Start a Shared Analysis

Manage my local building datasets

Building properties

Benchmarking with DOE Building Performance Database

Benchmarking results are out of date due to change of building filters. Please update the results. Update

Energy Conservation Measure (ECM) Packages

Editing Energy Conservation Measure (ECM)

ECM Cost

ECM Embodied Greenhouse Gas (GHG) Emission

ECM Efficiency Values

Editing Incentives / Rebates

Are there any Incentives/Rebates?

What kinds of Incentives/Rebates?

Type 1: Rebate Based on Savings

Type 2: Rebate Based on Application

Type 3: Rebate Based on Program Participation or Tax Exemption

Type 4: Rebate Based on Interest Savings

Detailed Incentives / Rebates Connection with ECMs

Type 1: Rebate Based on Savings

Type 2: Rebate Based on Application

ECM Packages Currently Running

ECM Packages Finished

Renewables: Photovoltaic (PV)

District Heating and Cooling System

Introduction

Load Profile

Describe Systems and Simulate

Results

Life Cycle GHG

Parameters of Life Cycle Greenhouse Gas (GHG) Emission

Acknowledgements

Relevant Publications

Utility Rate

Assign a utility rate to each building type

Simulation settings

Download retrofit analysis results

Download hourly load profile results

Local Building Datasets

Introduction to the interface

Variable definition and how they are calculated

Introduction

Data source

Interface

References

Resilience Analysis

Select an Extreme Scenario

Extreme Event Settings

Lasting Period

Simulation Results

Select simulation cases:

Select building types:

Benchmarking results are out of date due to change of building filters. Please update the results.