Trilliant introduced Analytics as a Service (AaaS), a new offering that enables utilities worldwide to further leverage data by providing deeper insights and understanding of that data. With AaaS from Trilliant, information gathered from data collection tools is deeply analyzed to enable forecasting, increase the management of resources, better predict outcomes, and more. This improved analysis leads to greater automation of processes, enhanced decision-making, and reduced energy loss, among other benefits.
AaaS is a subscription-based, cloud-independent platform comprising all of Trilliant’s analytical AI and machine learning offerings. Acting as a data scientist, AaaS complements and augments the capabilities of data sources, such as Trilliant’s Prime Energy Suite, the company’s multi-protocol, multi-network software suite for data management; Trilliant’s Unity Suite Head End System (HES) software platform; or other third-party tools. Data models and results from these source applications are consumed in AaaS, transforming them into information that supports intelligent decision-making and actions.
“Data is one of the most valuable assets for utilities, but many are unable to take full advantage of it due to limited resources,” said Greg Myers, Global Vice President, Product Management at Trilliant. “We know customers are facing many challenges as they look to improve the customer experience, optimize the grid, and more, and they have been looking for a way to better use data sources. With AaaS, utilities have the ability to more accurately collect information and use it to automate models and forecasting, saving them time and improving decision-making. Additionally, customers are not limited by any existing tools they have in place, or their environment. With the Power of Choice, they’re free to leverage AaaS in the way that works best for them.”
Trilliant’s AaaS in Action - Non-Technical Loss Detection
In the case of a utility that wants to improve its Non-Technical Loss (NTL) detection, AaaS can be deployed to routinely pull data from any data source – such as Trilliant’s own Prime Energy Suite databases or third-party software – providing a more accurate and detailed analysis.
NTL analytics from AaaS identifies premises with a high probability of energy theft or loss at the source, throughout a utility’s entire service territory. It operates on the principle that tampered or misconfigured meters record anomalies in voltages and energy (power) readings. Data from smart meters provide the necessary information for the NTL analytics engine to identify the location of each meter that’s exhibiting anomalous behavior, enabling utilities to visit the addresses to verify and remediate the theft. Based on the accumulated data, AaaS will automatically update and execute the NTL model from the previous day. The model’s results would then be published, via an API, to be consumed by Prime Energy Suite, or another third-party system. With AaaS, the model outcomes can be visualized in any business intelligence (BI) tool with no additional infrastructure required.
One utility located in the Caribbean recently piloted Trilliant’s AaaS in an effort to improve the accuracy of its data for NTL. Its goal was to reduce energy losses and protect revenue by identifying meters which were very likely to be the source of NTL, avoiding false positives and unnecessary on-site inspections. With AaaS, the utility was able to capture more precise information, thereby enhancing its models. Specifically, the model demonstrated the ability to classify the company’s customers with high and low NTL risk using hourly active energy and voltage meter readings. Additionally, Trilliant was able to validate the results against a list of identified irregular accounts. AaaS achieved an average accuracy of approximately 80 per cent, a drastic increase in accuracy compared with other solutions in the market.
While AaaS is cloud-based and can be deployed on any cloud platform including Azure, AWS and Google Cloud, it can also be hosted and managed on-premises – a model currently deployed by an electrical authority in Asia.
