Zhejiang, China

Longfei Liao


Average Co-Inventor Count = 3.0

ph-index = 1


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: **Longfei Liao: Innovating Smart Power Grids with Deep Learning**

Introduction

Longfei Liao is an innovative inventor based in Zhejiang, China, who has made significant contributions to the field of smart power grids. With a focus on optimizing power flow through advanced computational methods, Liao's work stands at the intersection of technology and energy management.

Latest Patents

Liao holds a patent for an "Optimal Power Flow Computation Method Based on Multi-Task Deep Learning." This groundbreaking method enhances the efficiency of smart power grids by utilizing deep learning techniques to process state data at specific dispatch moments. The approach involves amplifying collected data samples for training and applying various optimization methods to determine dispatch solutions under different conditions. By designing a sophisticated deep learning neural network, Liao’s invention allows for simultaneous task training, enabling the prediction of optimal solutions and feasibility determinations for power dispatching.

Career Highlights

Liao is associated with Zhejiang Lab, where he collaborates with other talented professionals in the energy sector. His expertise in deep learning and its application to power grid management has placed him at the forefront of innovation in this critical industry. His work not only contributes to the operational efficiency of power systems but also advances the development of sustainable energy solutions.

Collaborations

In his role at Zhejiang Lab, Longfei Liao collaborates with colleagues such as Gang Huang and Wei Hua. Together, they contribute to research and development efforts aimed at refining technologies that leverage artificial intelligence for better energy management. This teamwork is essential for driving forward the advancements needed in smart grid technology.

Conclusion

Longfei Liao's contributions to the field of smart power grids through his patented method demonstrate a significant step forward in integrating machine learning with energy systems. His efforts at Zhejiang Lab, alongside talented peers, highlight the importance of innovation in addressing energy challenges in today's world. The advancements that Liao champions will likely play a critical role in shaping the future of energy efficiency and reliability.

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