One workaround I found is that because I have my own domain (adam-s.ca) I can effectively setup my own custom email address. On my domains service I setup an email forward to pass on all emails sent to xxxxx.adam-s.ca and pass them onto my gmail account. This way I am not paying for a email server and still have access to professional emails ([email protected] :D). This is enough to setup a free Power BI account and have the ability to publish great dashboards.
I wanted to share some of my person dashboards that I run off of Power BI.
The first one is my aquarium monitoring dashboard which I use to monitor my KPI. Now its been a while since I did a water change so you can see my TDS is out of spec and I recently moved my turbidity probe to a more appropriate location to properly manage the tank. I have 3 quick cards to see if I am green or red and then further charts to show daily variances and the time series trend at the bottom. I have some alerts that go out when I go out of tolerance but as can be clearly seen I don’t necessarily change my water immediately. (Before anyone freaks out, I have a heavily planted tank so I have a healthy equilibrium that allows me to do infrequent water changes).
Next I have my temperature monitoring dashboard that I use to track 3 main temperatures: my room temperature, my sun-room temperature (and by extension my outside temperature) and my server rack temperature. There is not much to note but I do use the server rack temperature to ensure good operation of equipment. I don’t want to run my server to hot as that my cause early device failure.
Lastly I have my power monitoring dashboard, where I track my desktop computer, server rack and fridge temperature. I track my desktop power usage to understand costs associate with use and to track GPU mining profitability. Server rack power as an important one as well as server hardware is known to be a big power draw. I spent a fair bit of time optimizing power usage and this dashboard was important in tracking those changes.
Overall I have much more work related dashboards but these are the ones I run on my personal Power BI workspace.
With new power monitoring plugs (flashed with custom Tasmota firmware) I wanted to have a look at my server rack power usage and see if there were any savings opportunities. I ran a “top" command on the VM that had the highest usage and found that my database (PostgreSQL) was running higher than I think it should. I started to go deeper into the jobs running on the database and found some stuck system jobs. I was able to resolve most of them and implemented a query timeout to prevent anything from going to long and was able to go from a mean 104 W down to 90W presenting a 14 W savings. This may not be much but this server runs 24/7 and so will accumulate over time.
I made a quick I-chart to show the power savings the the optimization period where I was hammering the server to figure out what was going on. There are still some cyclic increases in power that are related to clean up jobs.
Many people assume perfect normal distributions when looking at data, but this is not always the case. An example data set is shown below where the data is clearly non normal and both the normal and non parametric tolerance intervals and means are shown. The mean is obviously the same but the 99% tolerance intervals show some key distinction. The normal tolerance intervals overestimates and unrealistically goes to -1.907 because it assumes a normal distribution and “equal” datapoints on both sides of the mean. This is not the case and the non parametric tolerance interval is obviously more accurate as it takes into account the “Real” aspect of the data.
I know many people just default to always assuming their distributions are normal but this is not always true and wrong assumptions tend to backfire when it comes to statistics.
I monitor temperature and humidity in my sunroom. An interesting trend can be seen when overlapping humidity and temperature. As the sunroom is an uncontrolled environment when temperature increases during the day humidity drops down but at night when temperature comes back down humidity comes back up.
Analyzing my aquarium total dissolved solids (TDS) a neat trend can be noticed. A water change was completed on the 9th which resulted in a dramatic drop in TDS but over time the TDS had risen back to its previous levels. As this the aquarium has an open top, water will evaporate and lead to an increased solids concentration. A water top up was completed on the 8th and there was a drop in TDS related to that as well.
A follow up for some of the takeaways of the previous post (http://192.168.2.65/understanding-fridge-temperature/). Repeating the experiment in a different part of the fridge showed some cleaner results. In-fact there were oscillations present at a much more realistic interval (30min). This was very surprising as this is a mechanical fridge with a thermostat and the oscillating period was dead on 30min almost as if it was computerized.
The temperature logger was able to go for 4.5 days on the external battery bank.
One of my Power BI reports has reached the top 1% of all Power BI reports for the whole international company. A few keys to getting my report to this point.
Value to users: I tailored many aspects of the report to be more suitable to users in different departments giving them a reason to use it and widening the amount of people interested.
Automated Reports: Many daily and weekly status and trends were automated through power automate where users could quickly see top issues and then links were added to go to the Power BI report for further trends.
TRAINING, TRAINING, TRAINING: Many users are still new to Power BI and are unaware of what data is available. Training sessions and more commonly training videos were a key in getting users more engaged and involved.
Following the basis of http://192.168.2.65/quick-temperature-logging-probe/ I setup a wireless temperature probe to monitor the temperature in my fridge because who knows what the temperature is when the dial is set to 3 😉 (there is a joke about a fridge that goes to 11 somewhere there).
Results were very surprising as the 95% confidence interval was so tight together I though something was wrong. Looking at the trend chart we see that there might be some oscillations if we tracked for longer but this was already a 3-4 day time frame O_o. It would be extremely shocking to me if there were oscillation with a period of 7-8 days.Follow ups.
Follow ups & Notes
Trial with different locations as obviously positioning further away from cooling coils will have higher temperatures.
Trial with larger battery bank to get large time frame trending
This was done on a cheap & old mini fridge so there might be value in testing newer hardware.
The fridge was heavily stocked with high heat capacity items so hysteresis may be playing a bit of a part here.
Higher polling frequency, though 15s should be more than frequent enough.
