SBIR Phase I: Utilizing Reinforcement Learning to Optimize Ocean Wave Energy Capture

Award ID: 2133700

The broader impact of this Small Business Innovation Research (SBIR) Phase I project seeks to facilitate the blue economys continued transition to a big-data paradigm. Currently, there is no cost-effective power solution for off-grid, small-scale, energy capture applications at sea. The project deliverables may benefit the commercial ocean sector as well as the Federal government and local municipalities by enabling cheaper and more reliable power at sea. This enabling technology may contribute to the ability for planners and decision-makers to anticipate and adapt to changing marine conditions, which will ultimately reduce costs and increase reliability for taxpayers. Additionally, to achieve its commercial objectives, the participating small business is committed to sustainability in its growth plan and aims to reduce carbon emission by working with local vendors and locally-sourced, recyclable materials. The small business will also continue its existing partnerships with local technical training/trade schools and workforce development programs to mentor underserved students and create jobs. This Small Business Innovation Research (SBIR) Phase I project seeks to leverage advanced artificial intelligence for optimizing power output. The project seeks to demonstrate the application of advanced machine learning techniques to improve the efficiency and energy capture, and reduce the intermittency, of renewable ocean-based power generation. The project enables adaptability by using an advanced control model methodology which adjusts the device hardware based on ambient environmental conditions for optimized performance. Due to the deployment environment, this project will capture training data under a laboratory setting, train the control model offline, and apply it in the field by leveraging edge computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Peter Atherton