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, building location and size for building energy efficiency research.
library(FAIRmaterials)
# Create R data frame for commercial and residential buildings
<- data.frame(
bldg_data "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
<- fairify_data(bldg_data, domain = 'building') output
*
from fairmaterials.fairify_data import
import pandas as pd
# create python data frame for commercial and residential buildings
= {'PremisesName':['bldg 680', 'bldg 686', 'bldg 352'],
data '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]
}
= pd.DataFrame(data)
bldg_data
# This will generate JSON-LD file for the example data in Python
fairify_data(bldg_data,'building')
This data is supported by the US Department of Energy’s Advanced Research Projects Agency-Energy (ARPAE-E) : [DE-AR000125].