Standardizing renewable data: How Energy Data Tagger is transforming signal mapping

Data is the foundation for smarter decisions and optimized performance across the renewable energy sector; yet, managing SCADA (Supervisory Control and Data Acquisition) signals from diverse assets remains a major challenge. Inconsistent naming conventions and fragmented data structures slow down analytics and increase operational complexity.

To address this, the innovation team at GreenPowerMonitor (GPM), a DNV company and global leader in renewable asset monitoring, developed Energy Data Tagger, a solution designed to standardize renewable energy signals and streamline data mapping. 

We sat down with Giuseppe Ferraro and Malcolm Heath from the energy data team at GPM to learn more about the origin, impact, and future of this new tool.

 

How did your years of experience in renewables shape the idea behind Energy Data Tagger?

Malcolm: Giuseppe and I both have over 15 years of extensive experience in renewable energy. Giuseppe began advising on wind turbine design, while I focused on analysing wind farm performance. In recent years, we’ve been part of GPM’s innovation team, where we identify opportunities to help customers and develop cutting-edge solutions.

The inspiration for Energy Data Tagger came from recurring feedback: working with data from multiple sites was a nightmare. Signal naming was inconsistent, making it incredibly time-consuming to locate and use the right data. 

Before Energy Data Tagger, what were the biggest obstacles in managing and standardizing SCADA signal data across renewable assets?

Giuseppe: In solar and battery storage, numerous manufacturers produce different devices for complete systems. This diversity drives innovation but also creates chaos;, there’s no universal standard for tagging data. The same piece of information might have multiple names across sites.

Imagine writing software to calculate total power generation across your portfolio. It sounds simple, but one site might call it ‘AC Power’, another ‘Power AC’, another ‘Power’, and another just ‘P’. Multiply that by hundreds or thousands of signals, and you begin to see the scale of the problem.

Can you walk us through the core technical innovations behind Energy Data Tagger? 

Giuseppe: Defining a standard set of signal names was the obvious solution but implementing it required deep technical expertise and meticulous attention to detail. Over the past two years, our team has identified and standardized more than 1,600 unique signals, ensuring there are no duplications or contradictions. This effort resulted in our comprehensive taxonomy: Energy Data Tagger.

However, taxonomy alone doesn’t solve the entire challenge. While we hope the industry will adopt this standard over time, and legislation like the EU Data Act is a step in that direction, full implementation will take years. In the meantime, we still receive data with non-standard, ad hoc names. That’s why we developed a way to translate or 'map' site-specific signal names to their standardized equivalents.

Doing this manually is extremely labour-intensive − a single PV site can generate thousands of signals. To address this, we created a semi-automated mapping tool powered by natural language processing (NLP). The tool interprets signal names and matches them to the correct taxonomy entry. Because AI isn’t infallible, we built in a human validation step, allowing users to review and correct mappings. Crucially, the tool learns from these corrections, improving accuracy over time.

As we’ve deployed this solution, we’ve seen mapping accuracy increase significantly. It’s made the whole process smarter and faster, saving our engineers hours, sometimes even days, every time we connect a new site.

 

 

How does Energy Data Tagger differentiate itself from other data solutions in the market?

Malcolm: Energy Data Tagger was built by renewable energy specialists for renewable systems. Existing standards like IEC 81346 didn’t cover the full range of signals used today. With responsibility for thousands of sites, GPM had the unique perspective to create a comprehensive taxonomy.

We also continuously test and update the taxonomy as we migrate sites to GPM Horizon, ensuring it evolves with industry needs. GPM Horizon is a cloud-based SCADA and analytics platform for wind, solar, and storage assets, delivering real-time monitoring and advanced performance analysis. 

The automated mapping tool is another differentiator. It’s fast, cost-effective, and tailored to real-world challenges. 

What key decisions or breakthroughs enabled the tool to scale effectively, and what benefits are users seeing in practice?

Malcolm: I believe the tool scaled effectively because we made the decision to use the Energy Data Tagger internally, which keeps the taxonomy relevant and up to date, and because we developed the automated mapping tool that saves our engineers hours, or even days, when connecting new sites. In practice, we’re seeing major benefits: adopting a standard taxonomy has made it much more efficient to develop analytics and to monitor and maintain data quality, reducing both time spent and the risk of human error caused by inconsistent naming. The mapping tool alone has cut up to 90% of the time it takes us to map a PV site within GPM. And, unexpectedly, it has also increased the amount of data we can work with - signals that were previously ignored because no one understood their tags now have clear definitions, allowing us to extract real value from them.

 

 

How do you ensure data security and compliance with industry standards?

Malcolm: For the taxonomy itself, we have incorporated elements from established standards such as IEC and RDS-PS, while adding significant enhancements to make it practical for today’s renewable energy systems.

Data security is a top priority at GPM and DNV. Our deployment platform uses multi-factor authentication, and all code undergoes rigorous analysis with specialized tools to minimize the risk of vulnerabilities. These measures ensure compliance and safeguard customer data at every stage.

Could you share a real-world example where Energy Data Tagger solved a critical challenge for a customer?

Giuseppe: The best example is GPM itself. For years, we have provided SCADA systems and hosted customer data. Initially, allowing variations in signal naming was manageable, but as portfolios expanded and analytics became more sophisticated, standardization became essential.

It took two years to develop Energy Data Tagger, and the results speak for themselves. Currently, we onboard customer sites faster than ever and deliver advanced analytics that were previously impossible without standardized data. 

Looking ahead, what’s next for the Energy Data Tagger, and how do you see the role of standardized data evolving in the renewable energy sector over the coming years?

Giuseppe: We are continuously enhancing Energy Data Tagger, and while the mapping tool currently supports PV and battery storage signals, we’re now training it on wind data and planning to extend its functionality to map device names as well as signal names. Beyond these upgrades, we’re exploring new ideas and actively seeking customer input to guide future development. Looking at the broader industry, I believe standardized data will become increasingly essential because data only becomes valuable when its meaning is clear; without standardization, it’s just a collection of ones and zeros. Common standards unlock advanced analytics and make the benefits promised by AI achievable, and with regulations like the EU Data Act coming into force, we anticipate that standardization will quickly become the norm. Over the next few years, we expect widespread adoption of data standards, and we are confident that Energy Data Tagger will play a leading role in that transformation.