Hey everyone, I’m currently working on optimizing my energy monitoring setup and I wanted to share my experiences and ask for some advice. I have several power sensors that record data almost every 5 seconds, which results in a massive amount of data points. While this level of granularity is impressive, it makes chart rendering extremely slow. I only need an average value every 5 minutes, so I’m trying to find a way to reduce the data without losing the essential information.
I’ve tried a couple of approaches. First, I adjusted the scan_interval to 120 seconds in the source sensor configuration, hoping it would reduce the number of data points. Unfortunately, this didn’t make a difference—it still recorded the same amount of data. Then, I experimented with adding a filter using time_simple_moving_average with a window size of 5 minutes. While this did smooth out the data, it didn’t reduce the number of data points being stored, which means the charts are still slow to load.
I’m curious if anyone else has tackled this issue and how they approached it. Are there alternative methods or configurations that can effectively reduce the data points while maintaining the necessary accuracy for the charts? Any insights or suggestions would be greatly appreciated! Let’s collaborate to find a solution that balances data granularity with performance.