There are a few questions that we get asked fairly frequently - we've tried our best to answer them clearly below:
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.
The optimal base temperature depends on the building. You can never determine it perfectly (degree-day-based calculations can never be that accurate), but using degree days with a base temperature that's approximately right for your building can significantly improve the accuracy of your calculations.
If you're uncertain about what the base temperature means, we suggest you read this introductory article. That should help you to make a very rough estimate of the base temperature of your building.
To get a more accurate estimate we recommend regression analysis (you'll probably need to read the whole article to make sense of it but, at the bottom, there are step-by-step instructions for determining the optimal base temperature).
Ideally you'd have your heating and cooling energy consumption metered separately. But, if that's not an option (e.g. because you have a all-electric building with one meter only), you'll need to make estimates...
"Baseload" consumption is a term that's used to describe the energy consumption that doesn't depend on the weather, and that's typically pretty constant throughout the year. If you can estimate your baseload energy consumption, you can deduct it from your recorded consumption figures to get an estimate of the consumption that's heating or cooling only.
You can often estimate the baseload by investigating the consumption over a period when the heating/cooling was switched off. Though the approach that's usually recommended is regression analysis.
Baseload energy consumption is a very approximate concept for most buildings. It's often important to estimate it and remove it from consumption figures, but it can be difficult to do this well. This article explains more about the relevant issues.
The best option would be to separate the heating and cooling energy consumption by metering them separately. But, if that's not possible, you might still be able to do some useful analysis, provided you have a seasonal pattern of heating and cooling (e.g. with heating in the winter and cooling in the summer). If that's the case, we suggest that you do two separate regression analyses - one for heating and one for cooling. But don't use the whole dataset for each of the analyses; instead, split your data into:
Do a regression analysis on the heating-only data (1), and a separate regression analysis on the cooling-only data (2). To improve your two regression analyses use the approach described here for finding the optimal base temperature (bearing in mind that it will probably be different for heating and for cooling).
Once you've done your two regression analyses, you'll have two formulae - one for heating and one for cooling. For each future period of consumption you'll have to decide which one of these two formulae to apply... It will depend on whether the period is predominantly heating or cooling - you, or an 'IF' formula you've created in Excel, could look at the HDD and CDD of the period to determine this. Or, if you're feeling ambitious, you could try weighting the results of your two formulae according to the relative proportions of HDD and CDD in the period.
When you're splitting your periods of consumption into heating periods and cooling periods, you might find periods that saw both heating and cooling (and HDD and CDD). It's very hard to do anything useful with data that includes significant amounts of heating and cooling energy consumption together. We suggest you leave these periods out of both your regression analyses. If you have a lot of periods like these, it might be an indication of one of the following:
One final note: the more that heating, cooling, and non-weather-dependent energy usage are mixed together in your metered records of consumption, the less reliable any degree-day-based analysis can be. If you've not yet read our Degree Days - Handle With Care! article, please do.
We don't actually have a database of degree days... Each set of degree-day data that you download from Degree Days.net 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 timescales 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 Days.net is more like a calculator: you tell it exactly what data you want, and it calculates it for you there and then.
Anyway, analogies aside, if you want to buy a database of degree days, we suspect that you'll be interested in the following two questions and answers:
Yes! The Degree Days.net API makes it easy to automate the process of getting data out of our system.
Yes! With Degree Days.net Desktop you can specify a big list of locations and download data for all of them into a single spreadsheet.
If you've seen this page before you might remember us talking about the possibility of a Degree Days.net 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 we decided in the end that a standalone desktop app would be better for downloading data - more user friendly and more powerful. And we think it's turned out very well!
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 Days.net calculates data in a way that takes into account the temperature variations within each day. This makes the 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. Though there's no real need to understand the calculation process if you're just using the data for your own calculations.
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 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.
Firstly, temperatures recorded by different weather stations at nearby locations can vary by quite a surprising amount. This is why it's good to use data from a weather station that's as close as possible to the building that's energy consumption you're analyzing.
Secondly, it's likely that the two sets of data you're comparing were calculated using slightly different methods. We calculate degree days using a more sophisticated method than many other sources.
Finally, it's possible that you're actually comparing the data from our site with population-weighted degree-day data. Population-weighted data can be useful for utilities that are doing simple aggregate analysis of energy consumption across a large region (e.g. the state of California), but it's not so good for analysis of the energy consumption of individual buildings.
We've made a whole separate page about degree-day-data calculation.
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 the 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.