This is somewhat similar and somewhat different to what I’ve written before, so sorry for any redundancy. I’d love some way to reasonably estimate how changes to my thermostat settings impact runtime, based on my own real data, with some way to remove the effect of other variables like outdoor temp differences day to day.
An example of what I’d hope to answer is the age old debate about setbacks, and whether it saves money (or at least runtime) to have a certain setback overnight, while at work, etc. Intuitively, it seems like there should be a way to get a reasonably solid answer on an individual basis, as it’s probably a different answer for each home and each hvac system.
I’d ideally like to run a group of settings for a week, a month, whatever, and then change them and let them run for a second week, month, etc. and have Beestat be able to tell me that one or the other was X% more efficient, correcting for outdoor temperature variations.
Basically, it seems to me that there are a huge number of settings and variables that will all produce adequate and subjectively indistinguishable comfort, but they can’t all be equally efficient.
Maybe this is too complex to be realistic, but then again y’all are the data wizards and I’m not. So just throwing it out there.
I like the idea, but let me expose a few problems:
There are too many variables. Ideally you want to do this in a short time span to keep the outdoor temperature consistent, but even then it’s so variable with cloudcover, rain, wind, etc that it would be impossible to do much with the data. If you averaged over longer periods to account for that then you just get different outdoor temperatures.
It’s a lot of work to actually plan and do all this analysis. Even with beestat helping it’s still going to be a lot of manual tweaking.
The amount of people that would use a feature like this would be so small (probably just you ) that it’s hard to justify.
However, I still like the idea. To make it worthwhile we have to account for the above. I think the solution could be AI. You could theoretically train an AI that has inputs for different weather factors, system types, home types, settings, etc and then either allow the user to change the inputs to look for optimizations or else just randomly change things and attempt to brute-force some recommendations.
Would it work? Maybe. It’s certainly worth a look and I would love to see the results. Will I do it? No clue…I’ve always found AI interesting and would love a project as a catalyst to learn more.
Hahaha I figured that might be the answer but then again I’m impressed by what you CAN do already so who knows.
My line of thinking was, if Beestat already displays an average temp for the day, and you only compare days with the same average temps, eventually (not sure how large a sample would be required), those intra-day variations would weed themselves out. For example, if through the month of January you run your system with a setback, and then through February you run at a constant temp. At the end of those two months maybe there were 6 days in January with an average temp of 34 and 5 days in February with an average temp of 34… Average out the runtimes of only those with a direct comparison and see if a meaningful difference emerges.
But I’m prepared to believe it’s a lot tougher than that. Thanks!
If you do it let me know. You could use the beestat API easily enough to scrape some of the data if you wanted to pull it down into a spreadsheet. Off the top of my head things I would also want to consider are: outdoor temperature, sunlight, precipitation, daylight hours, wind, humidity, indoor requested temperature, sensor placement, sensor participation, external heat sources (space heaters, ovens, etc), open windows/doors.
I’ve always been interested in studies like this, especially ones to find out optimal heat pump settings. Just never had the time or the will to collect all the data.
I’m currently air sealing and insulating my rim joist in my house, and have found areas of extreme cold air movement. So I know the air sealing and insulating will help.
What would be nice is a feature that could compare the pre vs post change slope graphs. This could be used for HVAC replacement or other insulation & air sealing projects.
My graph shows a little chaotic a month post change. Last week was actually showing a negative heat slope. Each weekly update clears out a bit of old data and replaces with new data. Looking at the standard temperature slope we see actual data (higher than 35 degrees, pre-change) is lower than the theoretical slope, so I know my change is very effective almost acting like new equipment or even a new house!
Would interested in being able to select two different time periods for comparing the heat/coo/resist profiles. Could be useful in quantifying the effects of home energy upgrades.
It would be very helpful if Beestat could be used to track and quantify home energy efficiency improvements (or downgrades for that matter).
I think the simplest implementation would be the ability to calculate metrics over a specified date range. However, I imagine this function could get pretty technical and therefore more informative. I’m always wondering how much large house projects affect energy efficiency!
It would be nice to add a marker to beestat to reset/compare the cool/heat delta per hour before and after new equipment is installed. My AC is original to the house and I found it was covered in dog hair and entirely clogged a few months after moving in. I cleaned it and I can visually see the runtime is better, but it’d be nice to actually see the difference in real numbers. Same goes if I ever decide to replace the unit, would be great to see how much of an improvement is actually made.
This is actually coming. I’m not sure how it will manifest itself, though. Some background:
The cool/heat delta per hour is part of your thermostat profile (mostly the Analyze/Compare tabs). That profile is built from the past year of high resolution data I get from ecobee.
In order to keep the database manageable, I purge data over a year old. Until recently, it wouldn’t have been possible to generate old profile data on the fly simply because I didn’t have the data.
A few weeks ago I started storing historical profiles. So at some point in the future I intend on adding a way to “revisit the past” and see or compare old profile data.
It would be helpful to add notes when you make changes\tweaks to your setup and show a line in the graphs for each comment. It makes it easier to look back and see what difference specific changes make. A couple of examples.
Like most people I have many variables in this complex system we call “house” and beestat is the perfect place to track and analyze these changes.
Mine’s a single zone system with dampers in almost every room which I change depending on season, which kid is home from college, and other scenarios. There are some really problematic rooms and I need beestat to help me prioritize my fix-it list: “Should I replace the living room roof, disable reverse staging on the ecobee, enable accelerated cool on the Bosch IDS 2.0 ASHP, adjust the dampers to force more air upstairs?”
Beestat may not be able to magically spit out the answer, but it could help me approximate an answer!
Frank’s idea would be super, super helpful first step: set markers w/notes as to why, and then later go back and notate the effectiveness of the change so I can learn for next time, or look back a year later to compare with my latest idea.
Ideally beestat would also allow us to separately analyze (include/exclude?) the time frames when the system profile changed for a couple weeks due to a parameter change, major weather event, setpoint or damper change due to house visitor.
More examples: Nor’easter left snow on roof for 3 days. Opened damper in kids room. Closed first floor trunk damper half way for summer. There are so many scenarios!
) that it’s hard to justify.