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Form 424B3 Nexscient, Inc.

Form 424B3 Nexscient, Inc.
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University Of Houston , United States , Saudi Arabia , Los Alamos , New York , Grand View , Newport Beach , University Of Southern California , Flavio Jason Orr , Asia Pacific , Leonard Panzer , Miguel Buencamino , Cara Miel Rodriguez , Ezzat Jallad , Tarekn Shoufani , Robertd Finch , Stephen Salzstein , Van Wadlington , Koji Kawana , Marcos Hume , Amy Gusick , Dominiquer Laurin , Davide Tannous , Thomas Owens , James Mcmiller , John Edward Gusick ,

"A new framework integrating reinforcement learning, a rule-based exper" by Xinlei Zhou, Han Du et al.

This study presents a new framework that integrates machine learning and a domain knowledge-based expert system to improve building energy flexibility. In this framework, a rule-based expert system was used to maximize the self-consumption of solar photovoltaics (PV) power, while a reinforcement learning (RL) agent was constructed to efficiently optimize the grid power import for battery charging and facilitate decision-making for battery discharging in response to the time of use electricity prices. Meanwhile, a Classification and Regression Tree (CART) model was developed to quantitatively analyze the relationships between building energy flexibility and external variables of interest to enhance the explainability of the framework. This work integrates the advantages of safety and simpleness of the domain knowledge-based expert system and the exploration and optimization capability of RL into building energy management. The performance of the proposed framework was evaluated using th ....

Regression Tree , Building Energy Flexibility , Net Zero Energy Building , Reinforcement Learning , Rule Based Expert System ,