Degree Days

Degree Days

Weather Data for Energy Saving

Degree – Frequently Asked Questions

There are a few questions that we get asked fairly frequently – we've tried our best to answer them clearly below:

Resizing columns in Excel

When I download the data I see ######## where the dates are supposed to be. What's gone wrong?

The # symbols just mean that you need to make the column a little wider in Excel. Excel shows # symbols when a column isn't wide enough to display the dates or numbers contained within it.

Position your mouse between the labels for column A and column B, hold the left button down, and drag the column wider. The animated screenshot on the right should help to make this process clearer. Alternatively you can double-click the mouse between the two columns to make Excel auto-fit column A.

Our national meteorological service offers degree days, would it be better to get the data from them?

Probably not. We are a specialist provider of degree days with a focus on energy saving. We calculate degree days as accurately as possible, and offer important specialist features for energy data analysis. Generalist weather-data providers have little incentive to do similarly, as their core focus is elsewhere.

Read more about the advantages of our system.

What base temperature(s) should I choose?

We say "base temperature(s)" because, for buildings with heating and cooling, you'll usually want one base temperature for heating degree days and a different base temperature (usually higher) for cooling degree days.

The optimal base temperature(s) depend on the building. You can never determine them perfectly (degree-day analysis can never be that accurate), but using degree days with base temperature(s) that are approximately right for your building can significantly improve the accuracy of your calculations.

If you're not sure what "base temperature" actually means, we suggest you start with our introduction to degree days.

Next, we recommend this article about choosing base temperatures – it should help you make a rough estimate of the base temperature(s) of your building.

To get a more accurate estimate we recommend regression analysis. This is easiest done with our regression tool – just go to the Degree web tool, select "Regression" as the data type, and follow the instructions from there. This will give you a shortlist of regression models (each with different base temperatures) that give the best statistical fit with your energy-consumption data. Use your estimates from the above-mentioned article on estimating base temperatures to help you choose the regression model with base temperatures that make most logical sense for your building.

How can I prove how much energy we've saved?

We've written an article explaining how to calculate/prove energy savings. This is a very common use of degree days for people who have made efforts to reduce energy consumption in their buildings.

How can I separate my heating/cooling energy consumption from everything else?

The ideal solution is to meter your heating/cooling energy consumption separately from everything else. But we appreciate that this might not be a realistic option for you at the moment.

The second best option is to use regression analysis to effectively split your combined energy-consumption data into heating/cooling and "baseload" energy consumption (the energy consumption that doesn't depend on the weather, and that is fairly constant throughout the year).

Our regression tool makes regression analysis pretty easy, and offers important features that can't easily be reproduced in Excel. It will help you find a good regression model, with an equation that describes your building's energy consumption in terms of heating (if appropriate), cooling (if appropriate), and non-weather-dependent baseload consumption (described by the constant c coefficient in the regression equation). Assuming you use the regression tool with weighted day-normalization (the default recommended option), the c coefficient in the regression equation will represent the baseload energy usage per day.

Do note that this process will only work well if you choose a regression model that has appropriate base temperatures for your building. This is very important when choosing which regression model to use from the shortlist that the regression tool will give you. Without appropriate base temperatures you can easily end up with a regression model that works well for total combined energy consumption, but does not split the total consumption accurately into its heating/cooling/baseload components.

If your heating/cooling is ever switched off for a full day or more, but in other regards the building operates as usual, the consumption on such days should represent baseload consumption only. If you can measure it (or work it out from energy-usage data that you have already), this estimate of daily baseload consumption can help you choose a regression model. You'd be looking for one with a similar value as its c coefficient (as well as having sensible base temperatures of course).

Please also bear in mind that baseload energy consumption is an approximate concept for many buildings. Our Degree Days – Handle With Care! article explains this in more detail.

What should I do when I've got both heating and cooling energy consumption metered together?

The best option would be to separate the heating and cooling energy consumption by metering them separately. But, if that's not possible, we recommend multiple regression of HDD and CDD together. Our regression tool does this automatically, and will also help you find appropriate HDD and CDD base temperatures by testing thousands of regressions with different HDD/CDD base-temperature combinations to find the ones that give the best statistical fit.

Just go to the Degree web tool, select "Regression" as the data type, and follow the instructions from there.

Can we buy your database of degree days?

Not exactly, but we do provide tools for you to build your own database. We'll explain further:

We don't actually have a database of degree days... Each set of data that you download from Degree has been freshly generated from raw temperature data, just for you. It's this on-demand approach that enables us to offer all the options that we do (thousands of weather stations, lots of base temperatures, a choice of breakdowns etc.).

This might not be the best analogy, but here goes anyway... Most sources of degree days are like times tables: the results are calculated in advance for a fixed set of numbers (or locations and base temperatures). Degree is more like a calculator: you tell it exactly what data you want, and it calculates it for you there and then.

So we don't have a ready-made database to sell you, but we do provide tools that would make it easy for you to build and maintain your own database. The following two questions and answers explain further:

Is there a way for our software to access the data from your site automatically? Like through an API or a web service?

Yes! The Degree API makes it easy for software to fetch data from our system automatically. You (or whoever develops your software) could program your system to fetch data on demand (as and when it's needed), or you could program it to fetch whatever data it needs to build its own database and keep it up to date as fresh data becomes available.

Find out more about our API or read an overview of all our products.

I'm not a programmer, but I want to download degree days for lots of locations at once. Is there a way for me to do this?

Yes! With Degree Desktop you can specify a big list of locations and download degree days for all of them into a single spreadsheet.

If you saw this page many years ago you might remember us talking about the possibility of a Degree Excel add-in. That was our original thought, as we wanted to take advantage of our experience developing this Excel-based energy-management solution. But, after much consideration, we decided that a standalone desktop app would be more flexible, more powerful, and easier to use. And it turned out well, fitting perfectly within an Excel-based workflow. You can copy/paste in a big list of locations from Excel, click a button to download all the data you need (with a user-friendly interface to help you deal with any problems), and export it all into a spreadsheet ready for your analysis in Excel.

We released the first version of the desktop app in 2012, and since then it has become popular with multi-site organizations (such as large hotel chains), energy professionals managing energy consumption for multiple clients, and academic/government researchers doing large-scale data analysis. Some have moved on to develop their own fully-automated systems that fetch data directly from our API (all desktop-app users have an API account that they can use for other purposes too), but many have found the desktop app ideal for the longer term, as, with it and Excel, they can do everything they need without having to involve their IT departments at all.

Find out more about our desktop app or read an overview of all our products.

Why are the latest degree days missing for the weather station I am using?

Degree days cover full days, and usually we make the data for a day available as soon as the weather station reports a usable temperature reading on the following day. The best stations (with good star ratings) report frequently, so the latest data for them will usually become available within an hour or two of the day ending in the station's local time zone. Lower-quality stations typically report less frequently, so there is usually a longer delay for those.

If a weather station usually has fresh data each day, but then one day it doesn't, it's almost certainly because of a problem with the weather station itself. We don't own or manage the weather stations, we just make use of the weather reports they broadcast. So, if some part of a weather station's equipment breaks, and it stops sending out usable weather reports, we can't calculate the latest degree days until the problem is fixed and the station starts reporting properly again.

Good stations usually get fixed pretty quickly. So you can just wait, maybe using an alternative station nearby in the meantime. If it's an airport, you could also contact them to let them know that their weather station isn't working. They will probably know this already, but they may be able to give you a timeline on the fix, and they may be encouraged to know that someone local values their weather reporting!

I've downloaded daily HDD and CDD for the same location and base temperature. On some days there are non-zero values for both – how can this be?

Outside temperatures vary throughout the day, so keeping a building at a constant internal temperature can often require both heating and cooling on the same day (e.g. heating in the morning, cooling in the afternoon). Degree calculates degree days accurately, taking into account the temperature variations that occur within each day. This makes our calculation process more complicated, but it results in degree days that correlate better with real-world energy consumption.

If you want to understand this better, please read our page on calculating degree days.

On a related note: using heating and cooling to maintain a constant internal temperature is bad for energy efficiency. Typically you want the internal temperature above which the air conditioning comes on to be a few degrees higher than the internal temperature below which the heating comes on. Assuming you're doing this in your building(s) you will typically also want HDD and CDD with different base temperatures.

I've downloaded data from your site and from another source (e.g. NOAA). The figures don't seem to match exactly – why is this?

First, please check that you are comparing data:

If everything listed above is the same, but the degree-day figures are different, it is likely that the other source calculates degree days with a simple approximation method instead of using the accurate calculation method that we use. This is especially likely to be the case if you notice that the figures differ most for days/months when the outside air temperature was close to the base temperature for much of the time (this is when common approximation methods typically perform worst). Please see our page on degree-day calculation methods for more on this.

Even if the other source is calculating degree days using detailed temperature data like we do, there are still plenty of reasons why differences can arise. Although the accurate calculation method we use is conceptually simple, to implement it well using real-world weather data is complicated, thanks to the many secondary issues like:

And these are just a few examples. A data provider can build a simple degree-day-calculation system that pays minimal attention to issues like these, and their data will probably look OK, mostly, but of course it won't be as accurate as it could be. In contrast we have spent years working on our processes to deal with these sorts of issues, and we are confident that we are handling them well, but there will always be room for improvement.

Also, for many of these issues there is no single correct approach, so it's quite possible for multiple systems to do an excellent job, but still give slightly different results. Ultimately it's just not realistic to expect different systems to give identical results – close is about the best you can hope for!

How do you calculate degree days?

We've made a whole separate page about degree-day-data calculation.

I've got hourly energy data – wouldn't it be better to analyze it using hourly temperature data instead of degree days?

You can get high-quality hourly temperature data from our system with a Degree Pro Solo/Team account (or, if you can program, with an API Standard/Plus/Premium account). Hourly temperature data certainly has its uses for specialist applications like HVAC system sizing and building simulation, but, for analysis of energy-usage data, degree days are typically a better choice.

We are big fans of fine-grained energy data such as hourly or half-hourly data – its analysis is really useful for identifying and quantifying patterns of energy consumption that are impossible to find in courser data like daily, weekly, or monthly figures. We even make a software package called Energy Lens that is designed specifically for analysis of fine-grained energy data.

However, heating/cooling energy consumption is a lot more complicated than the energy consumption of simple on/off electrical equipment like lighting. At a fine-grained level the relationship between the temperature outside a building and the energy consumption inside a building is neither simple nor direct. Buildings retain heat/cool in a manner that varies with construction and usage and is difficult to model accurately. There are complicated time lags (typically of the order of hours) between changes in outside temperature and the increases or reductions in heating/cooling energy consumption that result inside.

Heating/cooling systems often cycle too, operating in bursts that are unlikely to fall regularly within fine-grained metering intervals. And intermittent heating/cooling (e.g. a building that is only heated/cooled during office hours) complicates the fine-grained temperature/energy-usage relationship more as buildings that cool down (or warm up) when unoccupied need an intense period of heating/cooling (and energy consumption) to bring them back to temperature again. The energy consumption of that intense period of heating/cooling is only loosely related to the outside temperature during that period – it's much more dependent on the temperature changes that came beforehand.

These factors can be modelled, but doing so is the realm of sophisticated building-simulation software that requires detailed information about a building's construction, equipment, and usage patterns. If you try a simple regression of detailed energy data against detailed temperature data, the noise and complexity in the temperature-consumption relationship means you are unlikely to get useful results.

However, if you combine your detailed energy data into, say, weekly totals (a good option since most buildings operate on a weekly cycle), the noise is smoothed away and you will typically see a simple, direct relationship with degree days.

Also note that properly-calculated degree days encapsulate all the relevant variations within the detailed temperature data that they came from. So you're not losing detail in that regard.

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