Researchers Show How Inverter Data Spots Solar Faults Early

Recent research suggests smarter software could change how Australian households understand their solar systems — from production to consumption — without necessarily adding more hardware.

Can Inverter Data Reveal Solar Problems?

An Australian study has explored whether inverter data alone can spot solar production underperformance. Meanwhile, a separate study in Switzerland looked at estimating household electricity use from smart meter readings without dedicated CTs (current transformers). Together, the research hints at a future where software could give households a clearer view of what’s really happening in their systems, potentially helping them get better value from their solar.

Why Monitoring Matters

The “holy grail” for solar monitoring is seeing both solar generation and household consumption in real time, spotting system issues early, and getting tips to improve self-consumption. Most system owners only see solar production through their inverter app, which means performance issues and optimisation opportunities can easily go unnoticed. The stakes are even higher for households with batteries, where knowing when energy is generated, stored and used can directly affect savings.

Today, full visibility generally requires either CTs installed in the switchboard or a third-party monitoring platform that integrates smart meter data. Many users don’t see enough value to justify extra hardware or subscriptions, particularly if the system appears to be running smoothly. Standard inverter apps still mainly focus on solar PV output.

Aussie Research on Solar Production Monitoring

Scientists from the University of Technology Sydney, energy resources company Diagno Energy, and the University of New South Wales contributed to a new Australian study analysing AC-side solar production from over 1,000 residential systems across multiple regions. Using only standard inverter data, the researchers developed a rule-based workflow that compares expected daily output — based on system specs and local weather — with actual generation, flagging deviations that indicate common faults like inverter trips, extended zero output, or clipping.

This approach is novel because it can detect and classify underperformance without extra sensors or high-resolution measurements. The method identified most major faults with high accuracy — showing that, at least in this dataset, existing inverter data can be put to better use.

Overall, the study shows that manufacturers and monitoring platforms can use this workflow to spot problems and improve performance for systems already in the field. It’s a useful, incremental step toward smarter solar generation diagnostics, though it doesn’t provide appliance-level or real-time consumption insights.

Research on Consumption Data Without Extra CTs

Meanwhile, researchers at Switzerland’s Urban Energy Systems Laboratory explored whether ordinary smart meter data, recorded at 1–15 minute intervals, can estimate energy use for individual appliances — a technique known as non-intrusive load monitoring (NILM). They tested six algorithms — from statistical methods to modern deep-learning models — using different datasets and recording intervals.

The study found that deep-learning models like SGN and Seq2Point outperformed older methods, picking up both regular and changing patterns in appliance use more accurately. Performance still varied depending on appliance type and data frequency, showing that very low-frequency smart meter data can provide useful—but not perfect—appliance-level readings.

This research is relevant to Australia because it suggests that standard smart meters could one day give households a more detailed understanding of consumption without extra CTs or sensors, although the technology is still experimental.

How It Compares to Monitoring Right Now

In Australia, electricity networks already record household consumption through smart meters for billing and grid management, but these meters don’t provide real-time self-consumption or appliance-level visibility to the household.

Some monitoring platforms can combine inverter production with household consumption data, either via CTs or by accessing smart meter totals. These services still require additional setup and sit outside standard inverter apps.

Commercial platforms like Solar Analytics can show total household consumption alongside solar production, sometimes without additional hardware. However, they can’t break consumption down to individual appliances without CTs or other sensors, making the NILM approach in the Swiss study unique in ambition and scope.

The Takeaway

Combined, these studies point toward smarter, more data-driven solar monitoring. For now, getting the full picture still requires extra hardware or a dedicated platform, while standard inverter apps focus only on solar output.

Better software could one day give households a clearer sense of how their systems are actually performing. It may also give energy retailers a fresh opportunity to package “smart diagnostics” as a paid upgrade.

For those seeking deeper monitoring today, a dedicated platform remains the most practical option. To compare solutions, see SolarQuotes’ guide to solar monitoring.