Changes to the Purrmetrix webservice

It’s been quite a year.

Every bit of our service has expanded: customer numbers, project numbers, number of buildings, analyses and hardware served. Thank you to everyone who has used the service and given us very useful feedback.

Based on that feedback, it’s time to make some changes to the webservice. Our aim here is to make it more intuitive and to add some extra features.

The changes will go live over the next couple of weeks, so let’s take a little tour:

1.Webservice layout

The most obvious difference is that we have moved the projects menu and the views menu up into the top bar. This frees up the full screen width for visualisations and analytics. To switch between projects or to add a project you now need to click on the drop down menu in the top bar.

Can’t see what you are looking for? The drop down menus support scrolling.

Webservice temperature analytics

You’ll still be able to name the project and set it up in the project dialogue box (which you enter by clicking on the title of the project).

Similarly, if you want to set up a new analytics view, you click on the drop down menu and select the view you need. It will open in the main screen and you can add your kittens from your libary (the section with ‘Your Things’ at the top) or from other views.

Temperature analytics webservice

2. Checking (and removing) kittens

As before, if you have a kitten in your hand and want to know which one it is, you can squeeze its face and the kitten in the webservice will turn red:

Temperature analytics webservice

BONUS TIP – for power users if you activate the magnet at the top right hand corner of each view, every time you squeeze a kitten it will add itself to the view.

Removing a kitten from a view is also simplified – when you pick up a kitten from within any view, a trash can will appear in the bottom right of your screen. You can drop kittens in them and they will disappear from the view you had selected them from. Note they will stay in all other views in the project, UNLESS you take them out of the project library view (at the top) and trash them. This will cause that kitten to disappear from all views in the project.

3. Scrolling through graphs

As well as making graphs much quicker to deliver information, we have activated a click and drag zoom to help zoom in and out on graph data. Here’s how it works: you place your mouse in the middle of a graph, click and drag either backwards (left) to zoom out, or forwards – right – to zoom in. The longer you drag for, the bigger your zoom.

Temperature analytics webservice

4. Mean/max/min for graph views

For graph views, we have added ‘summary’: the ability to track the mean, max and minimum of any group of kittens over time. The feature can be turned on in the view dialogue box, which you get to by clicking on the view title.

Temperature analytics web service

Selecting ‘mean’ here produces this:

Temperature analytics web service

Helpful if you need to find out what the average performance across a zone is or track the impact of an improvement that affects many areas.

5. Project addressing

Projects and teams can both now hold address information. In future, this will allow your projects to be mapped in Google maps and potentially integrated with other localised information.

6. What’s next?

We will push these changes live in the next two weeks and look forward to your thoughts. And if you’d like to report problems or suggest improvements to performance, please do get in touch. We love to hear from you.

 

 

 

“Where should we put the sensors?”

Of all the questions we get asked, “Where should we put our kittens?” or “How many should we buy?” comes up the most often.

Answer: That depends what you want to know.

Look, I know that can sound evasive but my Christmas break proves my point. Some of you might already have read about the monitoring of 2 very different houses over the Christmas period and what we found. We saw some very interesting things in that experiment and we’ll pick up a few of those over the next few months.

At the 70’s house that we monitored I found something a bit odd. We were all sat around the laptop on Christmas Eve looking at what was happening and to see if anything surprised us. My parents have lived in that house for 24 years so we saw pretty much what we expected, however the main family bathroom intrigued us. It’s located at the top of the stairs, has a double glazed window and a fairly large towel rail style radiator. We have never been consciously cold in that room so why was it 17 degrees?

The sensor was on a fully tiled window sill right next to the window at about waist height, so we ran a little test and moved it to the top of a wooden bathroom cupboard at about head height and the temperature climbed by nearly 5 degrees! We all knew that it didn’t feel like 17 degrees in there but we never expected such a huge difference in temperature by moving the kitten less than 1m within the same room.

Screen Shot 2017-01-04 at 15.00.15

We can occasionally be surprised about what we see when we put sensors in environments we thought we already understood, we see in real terms just how sensitive the kittens are and how temperature can change very quickly across space.

Which presents a useful tool for comparing performance. For example: my parents upgraded their windows to double glazing in a few separate rounds to spread the costs not long after they moved into the house. This window was one of the first ones to be installed in the house and are almost certainly getting on for 20 years old. Installing a larger number of sensors in some of the rooms to monitor exactly how well the double glazing is performing and even compare the windows installed at different times could identify if some or all of them could benefit from being replaced.

We also saw things that we expected like peaks in temperature when we had showers and when the heating turned on and off:

Screen Shot 2017-01-04 at 15.03.09

We used humidity kittens across the house, some of you might be interested in seeing the graph for humidity across the same timescale. I managed to achieve 96.9% humidity with my post run shower. (In particular, you’ll notice the humidity continued to rise after my shower? This is a function of the way we measure humidity – as relative humidity – which we’ll cover in a later blog post).

Screen Shot 2017-01-04 at 16.07.18

So, how can this help to inform you on where to place your kittens? Like we say, it does depend heavily on what you want to know, but here are some general guidelines:

  1. If you want your kittens to record the temperature that you feel in a room them make sure that you place it well away from any heat sources such as radiators, air con units or fans that blow warm air out of computers etc.
  2. If you want your kittens to record the temperature that you feel in a room then make sure that you place it well away from any sources of cold such as open door, windows, draughts or on cold materials such as tiles.
  3. It you want to record the temperature of in a specific place then be sure to locate the kitten exactly where it’s needed, they really are very sensitive i.e. next to a thermostat to check its functionality.
  4. Try to avoid putting kittens in direct sunlight as they will be effected by solar gain, unless of course this is what you are trying to measure.

Remember that if you want to see in more details what’s going on in a room then re-deploy your kittens or buy more, one of the main advantages of how small they are is that you can put them pretty much anywhere. Place them in a grid pattern around the room or if you are feeling really adventurous hang them off fishing line  at different heights to create a cross section of your space. This can be very interesting particularly in a mezzanine or tall staircase, you might be amazed to see whats going on.

If you want to know more about how Purrmetrix can help you to learn about your space then contact us by email at help@purrmetrix.com or call us on 01223 967301

 

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!

Too hot/too cold – what can you do next?

Create your own user feedback survey

Picture of the month: HVAC in hot weather

So summer is icumen in here in Cambridge. Here it is certainly following the traditional pattern of an English summer: three days of heatwave followed by a thunderstorm.

All of which has produced some hard times for the cooling systems in the facilities we are monitoring. Take this:

Air conditioning failure summer

July 1st was the hottest day of the year so far, with temperatures over 32° following on from a couple of days with both warm weather and many hours of sunshine. So not surprising that at 12.15 the HVAC in this office gave up under the strain and couldn’t be bought back online until 15.30.

Picking this apart raises some fun details and questions. It’s worth comparing that week to the week before. Here are the plots for the Monday, Tuesday and Wednesday with the temperature and sunshine plots for those days along side:

Air conditioning failure 29th JuneAir conditioning failure 30th JuneAir conditioning failure 1st July

And here are the plots for the same days (monday, tuesday, wednesday) on the preceding week.

Office air conditioning summer 3

Office air conditioning summer 2

Office air conditioning summer

In this case temperatures overnight in the preceding week were much cooler – you can see the cooling system is working for much less time in the morning to remove the heat. There’s no characteristic sharp drop in temp at 5.30 am.

By the time we get to the week of the 1st July though, external overnight temperatures have risen and the office is holding a lot of heat overnight. In the morning removing that heat is taking more and more time. Furthermore, evening sunshine on both the two preceding days has warmed a portion of the office (on the right hand side of plan) that has always had difficulty in removing heat, so the system was under an immense amount of strain.

Under the circumstances if the system was going to suffer a failure, this is the situation which is likely to trigger it.

After the temperature topped out at 28.2, the cooling was eventually bought back on line, but the damage was done: there was far too much heat in the building to be removed by the HVAC before it was turned off at 7.30 and another warm night overnight meant the repaired system had to work for a much longer period in the morning to remove it, churning away for twice as long as usual.

Lessons learned? If you have HVAC systems that are older and have already identified some hot spots, be ready to take drastic action if you have a period of warm nights and solar gain. You might be operating closer to the edge than you might think.

If you have similar troublespots in your facility and you’d like a better picture of how the summer is affecting them, contact us for a quote on a pilot deployment.