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TBD: Innovative Technologies to Solve PV Regulatory Challenges

Poster Presenter

      Robert Scheiner

      • CIO
      • iVigee
        United States


Understand how to: - Address challenges in Regulatory Intelligence gathering, validation, analysis, and dissemination. - Utilize AI-based classification and neural networks to automate an end-to-end Regulatory Intelligence pipeline. - Involve broader communities, regulators, non-profit/commercial partners, and technology groups to make gathering and validation of information efficient.


Many PV Regulatory challenges exist in how to efficiently collect, process, and disseminate Regulatory Intelligence information. E.g.: - The interpretations of regulations are not consistent making it difficult to compare regulations across regions. - The published regulations are often out-of-date. - Validating of local interpretations, as well as resolving of questions with NCAs, is often difficult, - Labor- and capital-intensive effort is needed to compile and record regulations globally (or even locally). - Tools are missing for deeper analysis of regulatory trends, developments, recent changes, or future expectations. - Distribution of intelligence to necessary stakeholders is ad-hoc or "newsletter-based" at the most. Such challenges apply especially when aiming for a broader and more flexible regulatory scope that responds to global needs yet maintains accurate interpretations and offers solid analytical value. To overcome these challenges requires latest innovative technologies architected into a modern scalable solution, and with a support of an open business governance model. This talk focuses on valuable lessons and challenges that a team at iVigee met and tackled since they fearlessly embarked on one such journey in 2021. It will be demonstrated: - How to efficiently address common challenges in Regulatory Intelligence gathering, validation, (near-)real-time analysis information and its dissemination, all with the help of advanced Machine Learning and Artificial Intelligence, utilizing supervised and unsupervised classification models and deep neural networks. - How such technology may be deployed in a cloud-based and scalable architecture to support an end-to-end automated Regulatory Intelligence platform. - What type of business model may support involving partners from community, regulators, or non-profit/commercial organizations, to make gathering and validation of information more efficient.