TBD: Innovative Technologies to Solve PV Regulatory Challenges
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- The published regulations are often out-of-date.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.