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GRIDSIGHT'S INSIGHTS Q2 2022 | GIS Data Enrichment Using Smart Meters

We discovered the most actionable data, network and customer insights in Q2 2022. This quarter it’s all about GIS enrichment.

At Gridsight, our mission is to accelerate the grid's transition to renewable energy. As we increase the number of electricity distributors we're working with across Australia and New Zealand, we're increasingly discovering new insights that many in the industry are often not yet aware of. 

Gridsight’s Insights is our quarterly highlights reel of what we’ve discovered while working with the most innovative and forward thinking electricity distributors in Australia and New Zealand. Below you’ll find the most actionable data, network and customer insights that we discovered during Q2 of 2022.

This quarter it’s all about GIS enrichment. We hope that you find this report useful. If you would like to learn more about any of the insights covered here, please reach out to us - we would love the opportunity to discuss them further with you.


Enhancing GIS Data Using Smart Meter Voltages

Network topology and geographic information system (GIS) data in low voltage (LV) networks, are prone to errors and inaccuracies. This can result from changes in the network state via switching, assumptions around network connectivity made within GIS software or commonly, that the data was never captured.

Historically, networks have not required accurate LV network data due to the “top down” nature of traditional network planning. However, modern networks require “bottom up” network planning to manage localised network congestion and hosting capacity limits for the increasing number of small scale distributed energy resources (DER) in the LV network. This means accurate LV network connectivity and topology is now critical to planning.

While improvements in LV network topology and connectivity data can be achieved via extensive field surveys, it is far more efficient and accurate to leverage other data sources, such as voltage measurements from smart meters, to enrich the GIS.By comparing existing GIS data and applying smart meter voltage data correlation, topology and connectivity can be validated, and outliers can be highlighted.

Fig. 1.1 shows a meter voltage profile (in red) that has a low correlation with surrounding voltage profiles (opaque) on what the GIS believes to be the same distribution transformer.

Comparing the same meter voltage with voltages on an adjacent transformer in Fig. 1.2, it is clear there is a significantly higher correlation in both shape and magnitude, indicating that the meter is in fact connected to the adjacent transformer.


Validating LV Network Open Points and Customer-to-Transformer Relationships

Two key use cases for improving GIS data with smart meters are verifying LV network open points and customer-to-transformer relationships.

Verifying network open points between adjacent LV feeders or substations can be achieved by analysing smart meter voltage profiles. Due to the diversity of load within the LV network, voltage profiles can vary significantly at the network edge. By correlating meter voltages either side of an assumed network open point, it is possible to verify the open point’s true location. For example, in

Fig. 2, if meter A has a highly correlated voltage profile with meter B and meter B has a low correlation with meter C, it is possible to deduce that the open point on LV Feeder #1 is on the pole further downstream of meter A on LV Feeder #2.

Similar logic can be applied to map customers to distribution transformers. By clustering meter voltage profiles based on their assumed parent distribution transformer, it is possible to verify whether customers are correctly mapped. When outlier voltage profiles are identified, they can be compared with profiles on adjacent transformers to determine their correct parent transformer.

It is important to note that both of these use cases do not require 100% penetration of smart meters. In fact, both can be achieved with voltage measurements from as little as two reference customers per substation or LV feeder.


Improved GIS Data can Decrease Planned Outage Over-Notification, Ensure Reliability for Life Support Customers and Future-Proof Dynamic Export Limits

Currently, due to lack of confidence in existing GIS data, it is common for electrical distributors to over-notify customers in an affected area ahead of planned outages. One key benefit of improved GIS data is the ability to more accurately notify customers when planned outages are expected to occur.

Additionally, inaccurate GIS data can lead to power outages for life support customers during planned works. Along with hefty fines for the distributor, there are serious potential ramifications for the health of these customers. Using available data to minimise the occurrence of these incidences should be of the utmost priority for all electrical utilities.

Finally, as Australia moves towards implementing flexible export limits for small-scale generators like solar PV, improving the accuracy of where customers are connected will enable the development of more intelligent and equitable flexible export strategies.


Gridsight helps electrical utilities transition to a decentralised grid by generating actionable, AI-powered network insights. These insights enable utilities to dramatically reduce the network augmentation required to safely and efficiently support more residential solar, batteries and electric vehicles. Based on CEO Brendan Banfield's PhD research, Gridsight was founded in 2020 to accelerate the transition to renewables.

Brendan Banfield

Co-Founder, CEO

Passion for research and innovation related to integrating renewable energy technologies into distribution networks using data.