Baking with your own data – Import views

If you’re analysing the performance of your building and its HVAC systems you probably already have data from other sources – meter data, building management system data, occupancy data, for example.

We at PurrTowers have been working for a while on ways to allow you to use other data alongside your kitten data. So we’d like to introduce our Data Import view.

Import

The Import view is available if you have an unlimited account (ie any tier above the free account). It allows bulk import of historical data, and attaches the data to a special external-data kitten. This kitten can be dragged and added to other views exactly as if it were one of our own sensor kittens and you can put this data alongside that collected by kittens.

How do I get my data in?

First create an import view as any other by clicking on the Import view in the Create Views selection:

Select

This will create a new Import view:

Import view

Data formats

Data is added to the import by dragging a data file to this view. The data file is going to get analysed before it gets imported to your account, so it helps to get it into the right format. Each line needs to include a time and date, and a value, with a comma between the two:

Datafile

This can be created using Microsoft Excel by saving a spreadsheet with two columns, one for the date time and one for the value, as a .CSV, comma separated value file. In order to get the date and time format right you’ll need to use a custom cell format which can be found in the cell format dialog box:

dd/mm/yyyy hh:mm

(For some Excel users you may find that this format doesn’t exist, but this link explains how to create the custom format you need)

Excel

OK, so you’ve got your data file sorted out, now drop it onto the Import view. This should put it to work, first uploading the file, and then checking the contents:

Analysing ….Found

Organising your Import Views

If everything went well then that Import view should tell you what it thinks of your data, and offer an import button that you need to press in order to get the data in. Once there just use the new Import kitten just as you would any other (yes, you can rename it just as you can rename the Import view).

We strongly recommend that you keep all your Import views in a dedicated project. This makes it easier to find each Import view when you want to add more data.

You can delete the Import view, it won’t delete the kitten or the data that you’ve already imported, you just won’t be able to add any more data to the kitten.

The small print – some important details

OK, as you might expect there’s a little small print:

You need one Import view for each thing that you want to import data for. Example: if you have three thermostats in a room you will need three Import views.

The Import view should happily eat several 100,000 points in one swallow. We have limited the import to 2MByte files at a time.

You can reuse an Import view as many times as you like, adding data from different times to the Import kitten to build up a complete history.

Sorry, but no, we haven’t worked out how to let you delete the data you’ve just imported. Please be careful to be sure that you are adding the data you actually want.

Yes, you can add data that might overlap the data that you’ve already imported. Our far-to-clever for its own good database will just average the data during those overlapping periods.

The Import view needs you to give it data points in time order. If you try to import data that is all backwards, or where some points are in backwards order then it will do its best but it will reject those points.

If the Import view cannot understand your data file it will warn you and let you try submitting different data:

Reject

Pricing

And finally – how much will it cost? We will charge the same amount for an import view as for any other kitten, so depending on the number of end points you are measuring prices will start at £10 per end point, per annum.

This is a new view that will be in beta for the next month so we welcome feedback and bug reports. Enjoy!

Working out the numbers – how many sensors do you need?

Often, when we’re talking to customers they will ask us how many sensors they might need. This is a great question because it lets us talk a bit about the applications customers are using the sensors for.

Honestly, we don’t have all the answers on this because we’re still breaking new ground here. There are a lot of potential applications for Purrmetrix that haven’t been tested thoroughly. That said I thought it might be helpful to explain a few rules of thumb we tend to use in answering this question.

TL:DR – it depends upon what you want to achieve with your project. Contact us if you want to talk through the specifics of what you are measuring

How specific is your HVAC analytics project?

Many customers start working with us in exploration mode. They want to identify and pick apart all the problems in their estate. In that case we suggest a fairly high density to start with – a sensor every 10 sq m or one for every cluster of desks. So in a fairly average 60 person office we’d be thinking about 15 – 20 sensors.

In a case where you want to collect data around a known problem, it’s generally possible to be a bit more precise about the numbers, depending on the type of problem you’re looking at.

What sort of problem are you hoping to test?

If you are looking at problems with specific parts of your building services – for example in each fan coil unit – then you have a fairly obvious guide of one or two per FCU. Although kittens can be redeployed its always better in our experience to test all parts of the system simultaneously so allow enough numbers to do that.

On the other hand, you may be interested in how the building’s fabric is performing – how quickly certain parts of the building heat and cool compared with outside temperature. If you think you have generalised insulation problems then 3 or 4 sensors along each aspect can generate quite a lot of information on the rate of heat loss, although you should allow for more if the materials change significantly along each aspect.

Are you analysing or influencing?

If you are hoping to influence behaviour (whether to save energy or helpdesk time) then you need to be presenting data at the hyperlocal level for each person. The ideal extreme would be one for every desk or working area, but in practise we find that a sensor within the same 10 sq m is generally adequate. Remember they can always be moved to accommodate sceptics!

How are you displaying it?

Because monitors are limited in how many pixels they can display the webservice has limitations in the way it displays the image of your building/project, which will be sized to 600 px wide. This generally means it is tricky to display very large areas with a lot of kittens, or make very precise placement of kittens on a low scale (zoomed out) plan.

Screen Shot 2016-10-23 at 20.55.14

At this scale it can be tricky to position this number of sensors correctly.

If you do have a project requiring a high density of sensors then make sure you zoom in and use the largest scale plan you have.

How the temperature gradients work

The colour gradient between kittens is not a reflection of the actual temperature in the gradient but of the confidence that it reflects the correct temperature. We don’t vary the size of kitten icon or the spread of the colour gradient so its spread is determined by the scale of the plan or image you upload the kittens above appear to be covering a floor area of around 6 sq m.

You can make the kittens disappear from the plan to better understand what is going on. Do this by clicking the arrow to the left of the view titla:

Temperature map of office

In general we don’t recommend uploading plans of any building of more than 60 meters.

In summary

  1. You don’t have to get it right first time, because you can redeploy your sensors
  2. Size your order to your use – general exploration and influencing behaviour will take more kittens
  3. Don’t try and display too many kittens in one heatmap view. Use other views (graphing) for that.
  4. Talk to us if you have a problem building and you’re not sure what the right level of diagnostics might be.

Four ways better data will improve your heating season

Are you getting the calls? Now its October, we’ve been noticing our heat maps warm up as our customer’s heating systems come on. For a facilities helpdesk, the summer’s steady diet of ‘too hot’ calls start to change – data from IFMA shows complaints of too hot and too cold run at the same rate in the autumn, so if your occupiers can’t make up their minds they’re not alone!

Getting your HVAC strategy right at this time of year can seem like an impossible task, but there are some ways to solve the conundrum and to set yourself up for winter.

Get a handle on temperature complaints

Your occupiers are confused. They have been accustomed to higher summer temperatures and while autumn weather fluctuates, their ability to adapt can’t keep up. So they are likely to feel different levels of comfort even where the temperature is acceptable. To add to the problems building systems may also struggle with temperature swings so some of their complaints will relate to genuine but temporary problems. What is needed is data of the real temperature for occupiers so that help desks can work out the right solution without calling out engineers every time. In this situation, data saves time and money, as well as the energy involved in constantly adjusting heating systems.

Setting up the right heating strategy

When to turn the heating on is a bone of contention in many work places. Some facilities managers run systems for short hours during the shoulder seasons to avoid see windows open and fans being used at the end of a warm autumn afternoon. Others simply aim for a lower temperature. The right answer will vary from building to building, depends on how much control you have, and how your building behaves. Temperature data help you spot the patterns of heat loss and decide which option will work best for your situation.

Call out the heating engineer

Underused over the summer, even well maintained heating systems can be temperamental when started up. A complete failure is easy to spot, but regional problems – broken TRVs in hot water systems, for example – can go unreported until you get into the cold months, leaving facilities managers with a series of small jobs which would be better dealt with in a batch. A comprehensive survey over the first weeks of the heating season to quantify all the problems will save time and complaints in the long run.

Finally, don’t forget the summer

Have you been fighting for budget to do something about HVAC problems all summer, only to have the exec team decide that since cooling is not now needed the decision can be deferred for a few more months? How do you keep making the case, when the ‘too hot’ complaints have died away? Collecting hard data on the extent of the problem defines the problem and creates the business case for intervention.

And in case all this data sounds intimidating or a potential time suck, take a look at our tools for collecting and working with it to tame your temperature problems. Or get in touch with your heating challenge.