In earlier posts, we mentioned a few daylighting studies for our office remodel. At this point the renovation is well under way and it’s time to get more serious about data logging. We’ve previously employed energy monitoring units to analyze overall electrical usage. Our next step from here is to study occupancy comfort.
To offer some background, the LMN office is in a beautiful International Style building from the 1950s. The floor-to-ceiling glass has its advantages, but glare and heat gain have always been a major issue at certain times of the day. Looking at the office floor plan (UDI for the year above), we notice the most prohibitively overlit areas during the afternoon (on the west side of the office). The old design placed workstations right up against the windows, requiring the user to reach over his/her desk to control the manual blinds. Monitors are also in the same plane as the glazing, so the contrast with daylight makes them even more difficult to read. This usually results in people closing blinds to avoid glare and then neglecting to open them in the morning.
This is a good example of how daylighting, glare, and user control of light levels have influenced sustainable systems as well as interior layouts. The old system wasn’t working, but the fix was a simple one. The new office will reconfigure desks so that the user does not have to reach over a large drafting table to reach blinds. Even better, we’re installing a Lutron dynamic shading system throughout the entire office. The setup will have an array of light sensors that will trigger shading schedules and greatly improve daylighting.
In the case of seating near the glazing, occupancy comfort was a glaring issue. There are also times when occupancy comfort can be more nuanced. We are not only designers but also stewards of our office, so we want to track the environmental variables of our workspace and log them to a database to track any changes over time. To do this we’re using Arduino and a host of sensors, mostly from SparkFun, to make office occupancy sensor pods.
The sensor pods will sense a wide range of data: temperature, barometric pressure, sound, light, humidity, carbon dioxide, and motion. We can make them quickly and affordably, which allows us to spread several throughout the office at typical workspaces. Each pod will be connected to a network and uploaded instantly to Xively. Xively (formally Cosm formerly Pachube) is an online service offering a database and interface for our results. This is a web page, so we can share this database with the public. We can also connect easily to Arduinos with the Xively library, and this library allows us to not only receive data, but also send data to the Arduino to trigger actuators. Just awesome.
Now, since we’ve put together a post-occupancy sensor using various hardware and our own code, we need to confirm the accuracy of our results. For this we ordered a HOBO sensor for benchmarking the custom setup. HOBO sensors are leaders in occupancy comfort data logging, and their hardware is top notch (as well as top dollar). With one HOBO, we can benchmark a fleet of the Arduino sensors and verify their accuracy. We’ve also ordered an SPL meter so that we may calibrate the Arduino sound sensor. As of now, we’re just reading analog values for the sound levels. Xively screenshot below.
After calibrating with the SPL Meter and Hobo, we’ll laser cut circuit boards to create a more permanent installation for the pod and place several in the office. In addition to evaluating occupancy comfort, we can also test these real world values against digital simulations from DIVA. In addition, we’re considering different ways of visualizing this data outside of the standard Xively interface. Stay tuned for more.