Energy
14 columns
110 rows
8 formats

Heating Degree Days (HDD) and Cooling Degree Days (CDD)

This dataset provides daily and monthly Heating Degree Days (HDD) and Cooling Degree Days (CDD) calculations across regions, including actual and normal values, base temperature settings, and population-weighted aggregations. It is designed for load forecasting, energy sales estimation, and climate impact analysis, supporting both granular and aggregated energy demand insights.

last updated 2 Nov 2025
by GoMask

The brief that made it

Utility load forecasting and demand planning

Sample rows

preview · 8 of 110 rows · all 14 columns

record_idstring	aggregation_methodstring	cdd_normalfloat	population_weightedboolean	data_sourcestring	region_namestring	region_codestring	base_temperature_ffloat	datedate	record_typestring	hdd_actualfloat	hdd_normalfloat	cdd_actualfloat	notesstring
US-NYC-20220101A	population-weighted	0	true	NOAA	New York City	US-NYC	65	2022-01-01	monthly	1200.3	1180.8	0	Typical January heating load
US-LAX-20220701M	other	655.3	false	Third-party	Los Angeles	US-LAX	70	2022-07-01	monthly	0	0	670.5	Tropical zone; higher base temp
US-CHI-20220301B	population-weighted	13.2	true	NOAA	Chicago	US-CHI	65	2022-03-01	monthly	850.2	830	12.8	Transitional month; mild cooling
US-HOU-20220814D	simple-average	25.2	false	Utility	Houston	US-HOU	65	2022-08-14	daily	0	0	27.4	Heat wave event
US-PHX-20230501C	population-weighted	380.1	true	NOAA	Phoenix	US-PHX	65	2023-05-01	monthly	0	0	390.7	blank
US-MIA-20220901F	other	600.6	false	Third-party	Miami	US-MIA	75	2022-09-01	monthly	0	0	620.4	Custom base temp for local study
US-BOS-20221201H	population-weighted	0	true	NOAA	Boston	US-BOS	65	2022-12-01	monthly	980.1	1002.9	0	blank
US-SEA-20221001J	simple-average	9.7	false	NOAA	Seattle	US-SEA	65	2022-10-01	monthly	220.8	225.3	10.1	Transitional month
US-DEN-20230101K	population-weighted	0	true	NOAA	Denver	US-DEN	60	2023-01-01	monthly	1050.6	1025.4	0	Lower base temp for local study
US-ATL-20220417D	simple-average	16.2	false	Utility	Atlanta	US-ATL	65	2022-04-17	daily	2.8	3	17.9	Spring heat event
US-BUF-20220301L	simple-average	0	false	NOAA	Buffalo	US-BUF	65	2022-03-01	monthly	930	940.2	0	blank
US-HNL-20230701N	other	670.6	false	Third-party	Honolulu	US-HNL	80	2023-07-01	monthly	0	0	690.3	Tropical zone
US-SFO-20220925D	population-weighted	16.5	true	NOAA	San Francisco	US-SFO	65	2022-09-25	daily	0	0	18.2	Late summer heat spike
US-DTW-20220201P	population-weighted	0	true	NOAA	Detroit	US-DTW	65	2022-02-01	monthly	1110.7	1092.3	0	Cold winter month
US-DFW-20230601Q	simple-average	420.2	false	NOAA	Dallas	US-DFW	65	2023-06-01	monthly	0	0	440.9	Early summer
US-PHL-20220501R	population-weighted	215	true	NOAA	Philadelphia	US-PHL	65	2022-05-01	monthly	100.8	98.2	220.7	Transitional month
US-CLT-20221223D	simple-average	0	false	Utility	Charlotte	US-CLT	65	2022-12-23	daily	42.1	40.5	0	Cold snap event
US-SLC-20230201S	population-weighted	0	true	NOAA	Salt Lake City	US-SLC	65	2023-02-01	monthly	950.4	945.9	0	blank
US-MSP-20230301T	population-weighted	4.7	true	NOAA	Minneapolis	US-MSP	60	2023-03-01	monthly	850.9	845.2	5.3	Custom base temp for cold study
US-PDX-20221101U	simple-average	0	false	NOAA	Portland	US-PDX	65	2022-11-01	monthly	420.8	430.6	0	Mild November

What the 110 rows show

from the 110-row sample

Other (aggregation method) stands out: mean cdd_normal is 597.2, against 78.1 for the rest.

46%population_weighted = true
16.4median cdd_normal
2record types
65median base_temperature_f
40.4median hdd_actual
38.6median hdd_normal

Mean cdd_normal by aggregation_method110 rows

cdd_normal110 rows, in bands of 100

Median 16.4, from 0.0 to 691.5.

data_source110 rows · 3 values

NOAA68
Third-party24
Utility18

14 columns by typefrom the column list below

string 7
float 5
date 1
boolean 1

Columns

14 columns in four groups

blueprint · 14 columns
column	type	description	example
Text 7 columns
`record_id`	string	Unique identifier for each degree day recordunique	US-NYC-20220101A
`region_name`	string	Name of the geographic region (e.g., state, city, or aggregation)	New York City
`region_code`	string	Standardized code for the region (e.g., FIPS, ISO, or custom)	US-NYC
`record_type`	string	Indicates whether the record is daily or monthlydaily · monthly	monthly
`data_source`	string	Source of the degree day data (e.g., NOAA, utility, third-party)3 sources · optional	NOAA
`aggregation_method`	string	Method used for regional aggregation (e.g., simple average, population-weighted)simple-average · population-weighted · other · optional	population-weighted
`notes`	string	Additional notes or comments about the recordoptional	Heat wave event
Numbers 5 columns
`base_temperature_f`	float	Base temperature in degrees Fahrenheit used for degree day calculation (typically 65°F)30 to 80	65
`hdd_actual`	float	Actual Heating Degree Days calculated for the period0 or more · optional	1200.3
`hdd_normal`	float	Normal (historical average) Heating Degree Days for the period0 or more · optional	1180.8
`cdd_actual`	float	Actual Cooling Degree Days calculated for the period0 or more · optional	0
`cdd_normal`	float	Normal (historical average) Cooling Degree Days for the period0 or more · optional	0
Dates and times 1 column
`date`	date	Date for daily records, or the first day of the month for monthly records	2022-01-01
True or false 1 column
`population_weighted`	boolean	Indicates if the degree day calculation is population-weighted for the region	true

Use it for

An energy dashboard
The population_weighted rate, cdd_normal by aggregation_method and a breakdown of data_source. Excel, Power BI or Tableau.
Why do the 18 other rows have a mean cdd_normal of 597.2?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Records110
US-NYC-20220101A0populati…
US-LAX-20220701M655.3other
US-CHI-20220301B13.2populati…
A software demo
Believable records with region_name, region_code and population_weighted to fill a screen in front of a buyer.

This dataset110 rows14 columns

Yours1,000 rows14 columnsregion_name: UK only

blueprint · heating-degree-days-hdd-and-cooling-degree-days-cdd

Behind this dataset

Same schema. As many rows as you need.

These 110 rows came out of a blueprint — 14 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Date and geographic region (service territory, state, weather zone)
Base temperature: 65°F (18.3°C) standard, custom for specific applications
HDD = max(0, base temp - average temp) for heating load
CDD = max(0, average temp - base temp) for cooling load
Population-weighted HDD/CDD for multi-city territories
30-year normal HDD/CDD for comparison to actual
Cumulative HDD/CDD for month-to-date and year-to-date
Correlation with natural gas sendout or electric load (kWh per HDD/CDD)

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: heating-degree-days-hdd-and-cooling-degree-days-cdd

What should your data show?

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Sample rows

What the 110 rows show

Columns

Use it for

An energy dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Energy

What should your data show?