Commercial and Residential Buildings

Commercial and Residential Buildings JSON-LD Description

The commercial and residential JSON-LD template is designed based on BuildingSync Schema and Building Energy Data Exchange Specification (BEDES) Dictionary. It defines several key meta information like climate zone, bulding location and building size for building energy efficiency research.

Use R pacakage FAIRmaterials to creating JSON-LD for XRD

library(FAIRmaterials)

# Create R data frame for xrd
bldg_data <- data.frame(
  "PremisesName" = c("bldg 680", "bldg 686", "bldg 352"),
  "OperatorType" = "Food Sales",
  "Longitude" = c(-75.27, -76.88, -78.52),
  "Latitude" = c(40.68, 41.17, 40.00),
  "City" = c("Easton",       "Montgomery",   "Bedford"),
  "County" = c("Northampton", "Lycoming",    "Bedford"),
  "State" = "PA",
  "PostalCode" = c(18045, 17752, 15522),
  "ASHRAE" = "5A",
  "KoppenClimate" = c("Dfa", "Dfa", "Cfa"),
  "FloorAreaPercentage" = 0.88,
  "FloorAreaValue" = c(6077, 4913, 5333),
  "OverallWindowToWallRatio" = 0.22,
  "ConditionedFloorsAboveGrade" = 1
)

# This will generate JSON-LD file for the example data in R
output <- fairify_data(bldg_data, domain = 'building')

Use Python package fairmaterials to create JSON-LD for XRD

from fairmaterials.fairify_data import *
import pandas as pd

# create python data frame for xrd
data = {'PremisesName':['bldg 680', 'bldg 686', 'bldg 352'],
        'OperatorType':['Food Sales', 'Food Sales', 'Food Sales'],
        'Longitude':[-75.27, -76.88, -78.52],
        'Latitude':[40.68, 41.17, 40.00],
        'City':['Easton',       'Montgomery',   'Bedford'],
        'County':['Northampton', 'Lycoming',    'Bedford'],
        'State':['PA', 'PA', 'PA'],
        'PostalCode':[18045, 17752, 15522],
        'ASHRAE':['5A', '5A', '5A'],
        'KoppenClimate':['Dfa', 'Dfa', 'Cfa'],
        'FloorAreaPercentage':[0.88, 0.88, 0.88],
        'FloorAreaValue':[6077, 4913, 5333],
        'OverallWindowToWallRatio':[0.22, 0.22, 0.22],
        'ConditionedFloorsAboveGrade':[1, 1, 1]
       }


bldg_data = pd.DataFrame(data)

# This will generate JSON-LD file for the example data in Python
fairify_data(bldg_data,'building')

Commercial and Residential Buildings

Tian Wang, Scott Maurer

2023-01-10

Commercial and Residential Buildings JSON-LD Description

Use R pacakage FAIRmaterials to creating JSON-LD for XRD

Use Python package fairmaterials to create JSON-LD for XRD

Commercial and Residential Buildings schema diagram

Acknowledgment