Introducing How I am Testing My Sense CSV Data with LLMs

[kevin1]

Thanks @Edison517,

The kinds of analysis I have played with the most with Sense export (CSV) are:

1. Look for Missing Data, especially in the mains (Usage) and solar (Solar Production). Those are a good indicator of general internet and Sense reliability.

2. If it looks like the mains and solar data is all there, compute and compare the net usage from Sense against the net usage from my PG&E utility net meter. The link below shows what these first two look like:

Happy 2025 / Goodbye 2024 - 6 Years In Review

Hi folks, It’s now 2025 and time to look back at 2024 (and earlier) in my Sense data. I export data every year and try a few different fun kinds of analysis on it. This year I decided to try a few new visualizations that focus on “missing” data. I have now accumulated 6 years of data on my second Sense (the first one died in 2019) and have Exported all 6 years ( = 52,000 hours) as .csv files at 1 Hour resolution using the Sense Export function in the web app. Here’s what the hourly data loo…

3. Then it’s useful to look at usage and changes in usage by device category, or the top 10 individual devices.

Stacked power plot for Always-on, ID's appliances, and other?

In the web app (home.sense.com), not the phone app, go to the Dashboard, then “View Detailed Usage”. Pick the time period you want to Export for - Week/Month/Year/Bill. Once you select, click on the Export in the upper right corner (in red circle below). You’ll then have the option of Hourly or Daily data. For the utility I described, pick Day (Daily). [Screenshot 2023-08-25 at 10.35.58 AM]

4. Finally I like to look at correlation between my Sense air conditioner (AC) detections and the AC runtimes coming from my upstairs and downstairs Ecobees.

Solving Sense Device Mysteries Using Correlation

Solving This Common Scenario A mysterious new Fridge 3 shows up ! How do you figure out whether it is related to any of the existing Fridges in your house, or any other device for that matter ? Mathematical correlation can help find the hidden relationships, probably more easily than overlaying waveforms.. I’m going to try to three comparisons using the Pearson Correlation coefficient. I’m not going to go into math, but the picture below is very intuitive - plot the energy/power usage of two …

I’m going to try to repeat this kind of analysis using Perplexity Pro. The main caveat is that I typically do my analysis on a yearly basis, but the 25MB file size limit of Perplexity Pro will likely keep me limited to monthly datasets.

ps: pretty impressive that you have the SDR up and running for water flow. I broke down when the local water companies started offering rebates and bought a couple of Flumes, which do similar. Had some fun trying to correlate them against my Rachio sprinkler runtimes here.