Solar panels leveraging photovoltaic (PV) technology to convert sunlight into electricity are notoriously inefficient. According to research by the International Energy Agency, one way to improve PV efficiency is through the implementation of Statistical Performance Monitoring combined with some advanced machine-learning.
In their report, the researchers identified 4 different methodologies for improving solar panel efficiency, all of which depend on constant monitoring:
The first system for residential solar analytics was developed in Australia, where solar irradiation data is made available free of charge by the government. This system comprises a simple energy meter installed on the PV system feed into the electrical power‐distribution box that collects data. Using statistical analysis, the data on generated electricity is compared to an expected generation profile from the irradiation data and system configuration. The system owner has access to real‐ time electricity generation data and fault diagnosis that identifies issues and what to check if performance was not as expected.
The second system uses machine learning to predict next day’s hourly production by small residential systems for aggregation into virtual neighborhood power plants for the benefit of grid managers. This system requires only inverter data feed to the system server. The algorithms work on the inverter feed and meteorological prediction extracted from commercially available meteorological servers. No irradiation data or system configuration data is required. Applying these algorithms on yesterday’s weather history, as opposed to weather predictions, produces an immediate indication of system health. Tracking daily system health, which is simplified to qualitative ratings from A to F, enables even the smallest system to positively ascertain that the system is performing as expected or that a service call should be made.
Fault prediction is the topic of the third system described in this report, which is also based on machine‐learning algorithms. Clustering statistical methods are used to predict future faults that will affect power production. This system requires only an inverter data feed and access to historical meteorological data extracted from commercially available meteorological servers. No irradiation data or system configuration data is required. This system has proven so far to predict future 9 loss due to faults, though work continues to classify the specific fault that will occur in order to enable the owner to undertake appropriate preemptive corrective action.
The fourth method is only theoretical it seems, and involves “application of artificial neural networks.” That’s a topic for another time…