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I am an enthusiastic Class XII student at Delhi Public School, R.K. Puram, New Delhi, with a deep passion for STEM and a strong commitment to academic excellence. I have consistently excelled academically, achieving a near-perfect SAT score, High Distinction in the International Chemistry Quiz by the Royal Australian Chemical Institute, and ranking among the top 10 performers in my class. I am a recipient of a state scholarship and multiple national and international science awards. Beyond academics, I hold a Diploma in piano from Trinity College London and enjoy playing basketball and badminton. As a keen researcher, I am especially interested in using AI-based technologies for water optimization, aiming to address critical challenges in sustainable water management.
Tell us what the water concern in your country is!Presently, India’s rapidly growing data center industry intensifies water concerns, especially in major cities already facing acute water shortages. Water-intensive cooling systems in these data centers further strain urban supplies, challenging sustainability in a nation with 18% of the world’s population but with only 4% of its freshwater resources.
This is what I think is one of the solutions for a sustainable future:I believe that adopting AI-driven solutions, such as a Deep Reinforcement Learning-based water cooling framework integrated with Sinergym and EnergyPlus, is key for sustainable data centers. This approach, proven in realistic settings, greatly enhances Water Utilization Efficiency (WUE), surpassing industry norms and advancing cooling efficiency and sustainability.
Effective Water Usage with Deep Reinforcement Learning in Data Centre
Data centers have been water guzzlers owing to heavy-duty cooling and energy requirements. Globally, very limited efforts have been made in water conservation and water utilization effectiveness (WUE) in data centers (DC). The industry standard for WUE is 1.8 L/kWh. This novel research is based on water conservation principle by improvising the WUE in DC. To achieve this objective this work proposes an efficient smart water cooling modeling framework that leverages the simulation software EnergyPlus (Open source platform) coupled with Sinergym (Open source framework) based on Deep Reinforcement Learning (DRL). In this study, a realistic DC has been leveraged to fully test the simulated solution for summer design days. The smart water cooling model optimizer has been trained using a…