Current market situation
To achieve the rapid acceleration of solar power plant implementation needed for the energy transition, it is necessary to manage and analyse the vast amounts of data generated by power plants every five minutes in an organised and well-structured manner. Unfortunately, this is not the reality today.
Inconsistent data-labelling and device hierarchies leads to difficulties collecting and comparing information from solar sites. For site owner and operators, it means employing software as a service companies is cumbersome and including new sites in portfolios is time consuming and error prone.
GreenPowerMonitor solution
At GreenPowerMonitor, a DNV Company (GPM), we have been undertaking an internal effort to standardise the data we store. We have designed our own data-driven taxonomy for metadata and timeseries data and organised this taxonomy into a device hierarchy. The taxonomy is data-informed, deriving from the 20M+ tags we oversee. To apply our taxonomy to our data, we have developed a natural language processing machine learning algorithm to match non-standard labels to our taxonomy with an accuracy of 90% on an evaluation set of 14000 unique data labels.
Taxonomy benefits
The taxonomy developed and tested by GPM, along with the machine learning application to apply the taxonomy to any database, will streamline data storage and processing. Once applied to a database, the new standardized taxonomy, which will also be updated periodically, will enable the comparison and analysis of data between sites, between portfolios, between countries and even between operators.
All about the new GPM taxonomy
We look forward to connecting with you and sharing our expertise in our new data-informed solar plant taxonomy and AI standardisation tool. Fill in the form to request a meeting with our team who will be available to answer questions, provide demonstrations, and offer insights on best practices.
