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pro vyhledávání: '"Poolla, Chaitanya"'
Autor:
Poolla, Chaitanya, Saxena, Rahul
The problem of learning parallel computer performance is investigated in the context of multicore processors. Given a fixed workload, the effect of varying system configuration on performance is sought. Conventionally, the performance speedup due to
Externí odkaz:
http://arxiv.org/abs/2110.07822
Publikováno v:
Sustainable Cities and Society (2022): 103984
Commercial buildings account for approximately 35% of total US electricity consumption, of which nearly two-thirds is met by fossil fuels resulting in an adverse impact on the environment. This adverse impact can be mitigated by lowering energy consu
Externí odkaz:
http://arxiv.org/abs/2004.06633
We present a nonlinear equivalent resistance tracking method to optimize the power output for solar arrays. Tracking an equivalent resistance results in nonlinear voltage step sizes in the gradient descent search loop. We introduce a new model for th
Externí odkaz:
http://arxiv.org/abs/1906.01718
Autor:
Poolla, Chaitanya, Saxena, Rahul
Publikováno v:
In Microprocessors and Microsystems February 2023 96
With the recent interest in net-zero sustainability for commercial buildings, integration of photovoltaic (PV) assets becomes even more important. This integration remains a challenge due to high solar variability and uncertainty in the prediction of
Externí odkaz:
http://arxiv.org/abs/1808.08657
With the rapid growth in renewable energy and battery storage technologies, there exists significant opportunity to improve energy efficiency and reduce costs through optimization. However, optimization algorithms must take into account the underlyin
Externí odkaz:
http://arxiv.org/abs/1808.06760
Publikováno v:
In Applied Energy 15 May 2019 242:1725-1737
Akademický článek
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Autor:
Poolla, Chaitanya
Publikováno v:
Dissertations.
By 2040, global CO2 emissions and energy consumption are expected to increase by 40%. In the US, buildings account for 40% of national CO2 emissions and energy consumption, of which 75% is met by fossil fuels. Reducing this impact on the environment
Externí odkaz:
http://repository.cmu.edu/dissertations/729
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1768&context=dissertations
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1768&context=dissertations
Autor:
Poolla, Chaitanya, Ishihara, Abe, Rosenberg, Steve, Martin, Rodney, Fong, Alex, Ray, Sreejita, Basu, Chandrayee
Publikováno v:
2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG); 2014, p1-8, 8p