TL;DR:
- Ecogy Energy, a DER owner/operator, is part of a research team awarded $230,000 by the US Department of Energy to advance AI research for solar energy applications.
- The project aims to apply machine learning technology to commercial and industrial (C&I) solar portfolios, reducing operation and maintenance costs.
- Ecogy, along with Stony Brook and PNNL, plans to enhance interoperability for devices and ML frameworks using an open-source platform.
- The goal is to optimize C&I portfolios by analyzing extensive real-world data, providing actionable insights and maximizing asset performance.
- Ecogy’s machine learning solution is based on the SolarNetwork platform, with the results to be released as open source for industry-wide accessibility.
Main AI News:
Ecogy Energy, a vertically-integrated Distributed Energy Resource (DER) owner/operator based in Brooklyn, has joined forces with esteemed research institutions to accelerate the advancement of artificial intelligence (AI) in solar energy applications. As part of a selected research team, Ecogy has secured $230,000 from the US Department of Energy Solar Energy Technologies Office (SETO), which has awarded a total of $750,000 for this initiative.
The primary objective of this project is to leverage machine learning (ML) technology to optimize commercial and industrial (C&I) solar portfolios and reduce operation and maintenance (O&M) costs. Given that C&I solar portfolios make up over 20% of the industry’s total fleet, with a projected three-fold increase over the next five years, the need for efficient asset management becomes paramount. However, the complexity and cost associated with implementing ML advancements have hindered progress for multi-vendor footprints.
To address these challenges, Ecogy has partnered with Stony Brook and Pacific Northwest National Laboratory (PNNL), pooling their expertise to develop an open-source platform. This platform aims to enhance interoperability for devices from multiple manufacturers and multiple ML frameworks, making ML accessible and feasible for diverse stakeholders. By promoting collaboration and synergy, this initiative seeks to unlock the untapped potential of C&I solar portfolios.
“We are laser-focused on optimizing the performance of our C&I portfolios,” asserts Jack Bertuzzi, CEO of Ecogy. With DER portfolios spanning the East Coast, including the US Virgin Islands, Ecogy has already laid the groundwork by establishing robust communication channels for monitoring and control across their assets. They have also standardized the dataset and enhanced resolution. Teaming up with Stony Brook and PNNL, Ecogy intends to meticulously analyze this extensive dataset, extracting actionable insights that will deliver immense value.
At the heart of Ecogy’s machine learning solution lies the SolarNetwork platform, open-source software designed to streamline system management and reduce costs associated with intricate topologies. By collaborating with Stony Brook and PNNL, Ecogy aims to equip the entire industry with a comprehensive toolkit, empowering asset managers to operate their solar assets more effectively. The results of these groundbreaking developments will be made available as open source, ensuring widespread accessibility and knowledge sharing.
“Our initial focus is to provide advanced ML techniques for solar plants, enabling asset managers to swiftly respond to new insights about their assets in real-time, or even before production-impacting events become noticeable,” explains Yue Zhao, the project leader at Stony Brook. Additionally, they strive to simplify the deployment of such software, eliminating the need for ML expertise and facilitating widespread adoption. Crucially, this project benefits from an extensive real-world dataset, continuously validating and refining their hypotheses.
Ecogy’s inclusion in the SETO Fiscal Year 2020 funding program signifies its commitment to advancing research and development in the solar industry. By lowering solar electricity costs, enhancing the competitiveness of American solar manufacturing and businesses, fortifying grid reliability and resilience, and exploring new solar applications, Ecogy and its partners exemplify the potential of harnessing US AI expertise, particularly in machine learning, to drive disruptive innovation.
Together, Ecogy, Stony Brook, and PNNL are on a mission to significantly reduce the “soft costs” associated with deploying and managing C&I solar assets, positioning them as a pivotal asset class within the renewable energy industry. By unlocking the power of advanced AI technologies, these collaborative efforts are poised to revolutionize the way we harness solar energy and pave the way for a sustainable future.
Conclusion:
Ecogy Energy’s involvement in this research team, along with its partners Stony Brook and PNNL, signifies a significant advancement in leveraging AI and machine learning for solar energy applications. By applying these technologies to C&I solar portfolios, the project aims to drive down operation and maintenance costs, optimize asset performance, and unlock the untapped potential of the renewables industry.
The emphasis on interoperability and open-source solutions demonstrates a commitment to collaboration and knowledge sharing, paving the way for widespread adoption and efficiency improvements in the market. This initiative holds great promise for accelerating the growth of the solar industry and establishing C&I solar assets as a highly relevant and valuable asset class.
