Energy Data Tagger solves issues arising through inconsistent SCADA (Supervisory Control and Data Acquisition) data, which is used to monitor, control and optimise renewable assets, by converting it into actionable insights.
Renewable energy plants generate thousands of data points every second, but these are largely labelled differently across manufacturers, which greatly limits automation and analytics. Consequently, without organised data, no insights on assets can be gained.
Energy Data Tagger delivers consistent, machine-readable signal naming that turns messy data into a standardised language for renewables. Trained on over one million signals from renewable assets (solar, wind, battery) monitored by GPM, Energy Data Tagger has now matured into a market-ready digital product.
It is the first software of its kind which operates as a standalone, standard driven tool rather than as part of a broader asset management system. The tool automatically classifies signals and asset names using Natural Language Processing (NLP), a form of AI which learns and improves over time. Using the vast training dataset, its tagging accuracy improves as it processes new data.
“The ability to standardise and analyse vast volumes of SCADA data is mission critical to the global scaling of clean energy systems and represents a major leap forward in technical innovation” said Juan Carlos Arévalo, Executive Vice President Green Power Monitoring & Solutions. “Until now, inconsistent signal naming across manufacturers and sites has blocked automation, analytics and portfolio-wide insights. Energy Data Tagger resolves that bottleneck by automatically translating disorganised data into structured, machine-readable formats. Built on standards IEC 81346 and RDS-PS and trained on over one million signals from renewable energy projects, the tool is designed to serve multinational portfolios and diverse asset types. As countries race to meet climate goals, tools like Energy Data Tagger help make clean energy not just possible, but powerful.”
Energy Data Tagger merges a robust taxonomy with AI-driven classification across solar, wind, and battery systems, transforming fragmented data into actionable clarity, offering:
Automatic naming that enables consistent and efficient labelling of hundreds of incoming data signals, either fully or semi-automatically.
Effective data analysis to allow real-time monitoring and deeper data analysis. It also enables integration and comparison of site data for holistic portfolio insights.
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