TBD: Innovative Technologies to Solve PV Regulatory Challenges
Poster Presenter
Robert Scheiner
CIO
iVigee United States
Objectives
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.
Method
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- The published regulations are often out-of-date.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.
