Richland, WA, United States of America

Ruisheng Diao

USPTO Granted Patents = 5 

Average Co-Inventor Count = 7.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Location History:

  • Richland, WA (US) (2021 - 2022)
  • San Jose, CA (US) (2020 - 2023)

Company Filing History:


Years Active: 2020-2023

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5 patents (USPTO):Explore Patents

Title: Innovator in Energy Management: The Contributions of Ruisheng Diao

Introduction: Ruisheng Diao, an accomplished inventor based in Richland, Washington, has made significant strides in the field of energy management through his innovative patents. With a total of five patents to his name, Diao focuses on leveraging deep reinforcement learning technologies to enhance efficiency and sustainability in various energy systems.

Latest Patents: Among his latest inventions, Diao has developed a groundbreaking patent titled "Deep reinforcement learning based real-time scheduling of Energy Storage System (ESS) in commercial campus." This invention utilizes a system with deep reinforcement learning-based control to determine optimal actions for essential components within a commercial building, thus minimizing operational costs while maximizing the comfort of occupants. This approach introduces an unsupervised deep Q-network method to address the energy management challenges, balancing the effects of operational costs against comfort levels while taking environmental factors into account at every time slot. His patent ensures that optimum control decisions can be made targeting both immediate and long-term objectives, incorporating a simultaneous consideration of exploration and exploitation.

Another significant contribution is his patent for a "Multi-objective real-time power flow control method using soft actor-critic." This invention details systems and methods to manage voltage profiles, line flows, and transmission losses within a power grid. Diao's approach utilizes an autonomous multi-objective control model that integrates one or more neural networks acting as a Deep Reinforcement Learning (DRL) agent. The DRL agent is trained to provide data-driven, real-time, and autonomous strategies for grid control, optimizing performance through a Markov decision process (MDP) to address dynamic and stochastic challenges.

Career Highlights: Ruisheng Diao has demonstrated exceptional expertise through his innovative research and development work. He has previously worked with the Global Energy Interconnection Research Institute Co. Ltd, contributing to various projects aimed at improving energy systems. His inventions reflect a commitment to advancing technology in energy management, as he continues to explore new frontiers in this critical field.

Collaborations: Throughout his career, Diao has collaborated with talented individuals, including notable coworkers such as Di Shi and Siqi Wang. These partnerships have allowed him to enhance his research and develop groundbreaking technologies that address contemporary energy challenges.

Conclusion: Ruisheng Diao's work in the field of energy management exemplifies the power of innovation through the intersection of technology and sustainability. With his five patents, he continues to make a substantial impact on how energy systems are managed in commercial settings, driving advancements that aim for efficiency and improved occupant comfort. His contributions highlight the vital role inventors play in shaping a more sustainable future.

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