You have me thinking about this some more - Two thoughts:
- A simple solar cell with a shunt resistor, even one that doesn’t have the same orientation as one’s solar panel system, would be a super-reliable feature to give local cloud input, to set daily goals.
- ML could be made to work if one looked at power output over the course of multiple days, where the length of time was sufficient to meet the required confidence interval.
Going to do a little experimenting with prediction using different windows when I get a chance. The interesting question to me is whether looking at a string of hourly data or a comparable string of daily data will give a quicker or more accurate predictor of a hard fail (near zero output from inverter).
BTW - Here’s a different view of my solar production over the past 6 years from a specific kind of time-series machine learning analysis, an STL (“Seasonal and Trend decomposition using Loess”) plot. It breaks down the observed time series in to 3 components, a seasonal component based on a yearly cycle, a trend line component that shows the long term change (panel dirt and degradation), and a random component, mainly due to clouds. It gives some great insights into possible triggers for alerting us to a broken inverter of panel (random component is negative and magnitude is greater than some value). Or trend line drops off more steeply ?
