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  • Data by AccuWeather.com

Your Source For Free Degree Day Reports

Degree day data is useful to indicate how seasonal weather affects building energy use, which impacts energy management, energy efficiency, and utility bill tracking.
Weather Data Depot includes:
 
  • Free heating degree day and cooling degree day data
  • Reports and charts for degree days, average daily temperature, cooling degree days, heating degree days, and degree day forecast
  • 14,000 weather stations in the United States and Canada
  • Data from weather expert AccuWeather
  • Easy Copy-to-Clipboard function to paste into Excel
  • Free website button personalized to your ZIP code

Frequently Asked Questions

What is EnergyCAP?

EnergyCAP is powerful energy efficiency software published by EnergyCAP, Inc. EnergyCAP helps you get a handle on your energy information and save money and energy ten ways. EnergyCAP is used by cities, counties, universities and colleges, Federal and state agencies, retail chains, property managers, manufacturers and businesses to: track and audit utility bills; identify billing, metering and building controls problems; assess the weather's impact on energy usage and cost; compare today's bills to a baseline year; perform rate/tariff analysis; process bills and campus chargebacks for payment; and produce hundreds of energy management reports, charts and benchmarks.

What is average daily temperature?

Average daily temperature is the "middle value" between the daily high and the daily low.

Example: Daily high = 70. Daily low = 50. Average daily temperature = 60.

What is a degree day?

A degree day is a measure of relative heating and cooling energy required by buildings. It's calculated as the difference between the average daily temperature and the balance point temperature (60 degrees). When the average daily temperature is above the balance point, the result is cooling degree days; when below, the result is heating degree days.

Example 1: Average daily temperature = 80. Balance point = 60. Cooling degree days = 20 CDD. (80-60=20)

Example 2: Average daily temperature = 45. Balance point = 60. Heating degree days = 15 HDD. (60-45=15)

Example 3: Average daily temperature = 60. Balance point = 60. No degree days.

You may ask, "Why not use average temperature instead of degree days?" The problem with average temperature is that highs and lows cancel each other out. A warm day (80 average temp) combined with a cold day (40 average temp) average 60. So do two mild days of 59 and 61. But in the first case there are 20 CDD and 20 HDD while in the second there is 1 CDD and 1 HDD. Using degree days, you can see that the relative amount of energy required for the first set of days is much greater than for the second set of days. But if all you looked at was the average temperature, you would conclude that both sets of days were about the same.

What is the balance point temperature?

The balance point temperature is the average daily outside temperature at which a building maintains a comfortable indoor temperature without heating or cooling. At this outside temperature, the indoor heat gains (due to people, lighting, equipment, etc) "balance" with heat loss through windows, walls, roof and ventilation.

Since the average daily outside temperature normally occurs at about 11 AM, here’s the question: On a typical day, if the outside temperature at 11 AM is 60, is the building being heated, cooled or neither? If heated, then the balance point should be set HIGHER than 60; if cooled, then the balance point should be set LOWER than 60; if neither, then a balance point setting of 60 is appropriate. See the next FAQ below for further discussion of balance point.

Why is a 60-degree balance point better than 65?

The 65-degree balance point standard was developed 75 years ago to help the gas industry predict heating loads in residences. Studies back then showed that when the average daily temperature fell below 65, residences began turning on the heat. To this day, many sources still track degree days using this standard, including the National Oceanic and Atmospheric Administration (NOAA).

Today's residences and commercial/institutional buildings are very different. Not only are walls, roofs and windows insulated much better, but also there are many more sources of internal heat gains (lights and equipment). Millions of degree day correlations by thousands of EnergyCAP users since 1982 have shown that a 60-degree balance point for modern buildings is almost universally more appropriate than 65.

Since residences have fewer sources of internal heat gains per square foot (occupants, lighting and equipment), you might find that 65 or even higher is a better balance point estimate, particularly in older residences that lack tight windows and high levels of insulation. In non-residential buildings, use a higher balance point (56-60+) for buildings that have low internal heat gains, high ventilation rates and poor insulation.

How can I determine the effect that weather has on my energy usage and cost?

EnergyCAP uses a linear regression technique to determine (1) if a building's energy usage correlates with the weather in summer, winter, neither or both, (2) about how much of the energy usage is due to weather and how much is "base load" that is not weather sensitive (lighting, cooking, equipment, etc), and (3) how weather sensitive the building is (in usage per degree day). The regression is usage (dependent variable) vs. degree days (independent variable) and yields a "predictor equation" in the form y=mx+b (predicted usage=slope x degree days + base load).

Explain the cumulative degree day charts.

The cumulative degree day charts are designed to help you quickly compare one year's weather to another as it relates to building energy usage. By comparing the cumulative degree days as of the end of one year with another year, you can quickly see which year was more severe (more degree days) as it relates to heating and cooling needs. For example, if 2005 had 4,000 cumulative HDD and 2004 had 3,000 cumulative HDD, you can conclude that the 2005 weather was 33% colder and would have required about 1/3 more heating energy. (EnergyCAP can help you calculate how much of a building's energy usage is consumed for heating/cooling vs. other non-weather uses.)

Why are some years missing for some stations?

Weather data recording stations sometimes experience failures. When we obtain the weather data, we look for missing data. If a single day is missing, we set the average daily temperature equal to the prior day. This approach is used for up to seven days. If more than seven days in a row are missing (about 25% of a month), we discard the entire year for that station as being unreliable.

I need Daily Degree Day data.

Simply click on Yearly Comparison Report, then click on a month name.

What is a Degree Day Forecast?

A degree day forecast is a 14-day forecast of daily heating and cooling degree days. The forecast is created and continually updated by AccuWeather.com. Each morning at 6 AM ET AccuWeather provides us with a forecast of mean daily temperature for hundreds of weather stations. We then convert the mean daily temperature to degree days using a balance point temperature of your choosing. (Default value is 60. See FAQ on Balance Point Temperature.) We display the forecasted heating and cooling degree days, the actual degree days for the same date range last year and the average degree days for the same date range in the last three years. The date range does not include today – it starts tomorrow and is for 14 days, inclusive of start and end dates. When the heating or cooling degree day total is zero (for example, the heating degree day forecast in late July) the charts will show zero days.

How is Future Energy Usage Predicted?

Energy usage within a home or non-residential building can be separated into two components: the energy usage that is sensitive to the weather and the energy usage that is not sensitive to the weather. In many cases, there will be no weather-sensitive component during the summer or winter (for example, a gas meter in the summer or an electric meter in the winter when the building has no electric heat).

If we know the non-weather component (expressed in usage per day) we can calculate the future non-weather usage. Example: my building uses 200 KWH of electricity per day for non-weather loads such as lighting and equipment. In the next 14 days my building will use about 2,800 KWH for non-weather loads.

If we know the weather component (expressed in usage per degree day) we can calculate the future weather usage. Example: my building uses 10 KWH of electricity per cooling degree day for A/C. In the next 14 days the forecast is for 200 cooling degree days, so my building will use about 2,000 KWH for A/C.

Adding the two together, the energy usage prediction is 4,800 KWH in the next 14 days.

Now comes the tricky part – how were the non-weather base load of 200 KWH/day and the weather factor of 10 KWH/degree day developed?

Using our EnergyCAP software:

We use single linear regression to statistically determine the non-weather base load and the weather factor. We first look at the start/end dates of each bill and add up the total heating and cooling degree days in the date range. We then reduce the degree days and usage to a per day basis and run a standard regression. This creates a regression line which is essentially a mathematical model of the energy usage of the meter in the form: y=mx+b where b is the y-intercept (the daily non-weather base load), m is the slope of the line (the weather factor in use/degree day), x is the independent variable (degree days) and y is the dependent variable (energy usage per day).

Using Excel:

You can use Excel’s built-in single linear regression function to correlate the dependent variable (energy usage) to the independent variable (degree days). Your results will be exactly the same as EnergyCAP. It is beyond our scope to describe the process in detail.

Using a simple estimation technique:

  1. Gather your last 12 energy bills (on a meter-by-meter basis).
  2. Look at the usage each month. Decide which best describes the trend:
    1. High in summer, low in other months. (weather sensitive in summer only)
    2. High in winter, low in other months. (weather sensitive in winter only)
    3. High in winter and summer, low in spring/fall. (weather sensitive in both seasons)
    4. About the same in all months, or wide variations between months. (not weather sensitive)
  3. If a or b: Calculate the non-weather base load as the daily usage in the low season. Calculate the weather factor: total the usage in the high months, subtract the non-weather base load, then divide what’s left (the total weather usage) by the total of the degree days for those months. The result is the estimated usage per degree day.
  4. If c: Use an approach similar to the above, only use the spring/fall bills to estimate the non-weather base load.

What is the location?

The location is the city and state, postal code, or database ID# of the regional weather data to display.

What is the base year?

The base year is the baseline calendar year to use in weather comparisons.

What is the comparison year?

The comparison year is the current calendar year to use in weather comparison.

What is the range begin year?

The range begin year is the first calendar year to display on yearly weather charts.

What is the range end year?

The range end year is the last calendar year to display on yearly weather charts.

What is the forecast begin year?

The forecast begin date is the current date on forecast charts.

What is the forecast end year?

The forecast end date is the date two weeks into the future on forecast charts.

Can I compare WeatherDataDepot (WDD) with National Weather Service/NOAA data?

Yes, we recommend http://www.nws.noaa.gov/climate/. Click on the U.S. map to select a region. (1) Select Product = Monthly Weather Summary, (2) Select the Location (note that WDD has many more weather station locations than are available from this NOAA site), and (3) Select Archived Data and Month Ending. In the Degree Day section of the report that pops up you will see month and YTD 65F balance point degree days and also prior year. You can also use the Daily Climate Report product to compare daily data.

Variations between WeatherDataDepot and NOAA may occur due to:

  • Different weather reporting station. Some cities have multiple stations in WDD (based on ZIP code or weather station code) whereas NOAA may only have one city listing, so you may be comparing different weather stations but have no way to know exactly which one is shown by NOAA.
  • Different balance point temperature. NOAA uses 65F, a standard established in the 1930s. WDD defaults to 60F because that is a better indicator of energy usage in modern buildings. Be sure to set WDD to 65F.
  • Data rounding. NOAA apparently always rounds x.5 up to the next whole number when it calculates mean daily temperature (MDT) from daily high/low. WDD uses the “Banker’s Rule” of rounding which is more appropriate in this situation. Banker’s Rule rounds x.5 up or down to the even number. In one month NOAA rounding can result in an average of 7 more CDD and/or 7 fewer HDD than rounding using Banker’s Rule.
  • Data conversion rounding. WDD receives rounded MDT data from AccuWeather. The NOAA source data is typically tracked in degrees Celsius with one decimal. The process of converting to degrees F and then arithmetic rounding to whole numbers on top of that can cause slight differences.
  • NOAA may change or correct data at future dates but there is no process to propagate these changes through to existing WDD data.
  • Can I get a list of weather stations?

    You can download an Excel document here containing all weather stations

    Why are some data points green?

    These points are pulled from an alternate weather source forecast.io.

    Why are some data points red?

    These points are estimated based on known good data.

    What is a Forecast station?

    Forecast stations are not solely dependent on observation stations only. Artificial intelligence and Accuweather proprietary techniques are used to generate forecast data for these stations. For historical data, each forecast station is mapped to a historical station that is geographically nearby.

    What is a History station?

    History stations are physical observation stations. These stations are used to feed historical data for forecast stations.