Optimization and sequencing of chilled-water plant based on extremum seeking control

Mu, Baojie; Li, Yaoyu; Salsbury, Timothy I.; House, John M.

doi:10.1109/acc.2016.7525272

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Since the power consumption of the chiller plant is a convex function of the condenser water temperature set point for a fixed PLR, the algorithm is guaranteed to find the optimal set point. In contrast to [168] where only condenser water temperature set point was considered, the cooling tower fan, the condenser water pump, the chilled water pump and the condenser water set temperature were jointly controlled via multivariate ESC [169]. To handle the constraints imposed on the inputs, a quadratic penalty function is added into the objective function that is the total power consumption of the cooling tower, the chillers and the water pumps.…”

Section: Energy-efficient Coolingmentioning

confidence: 99%

Toward a Systematic Survey for Carbon Neutral Data Centers

Cao

Zhou

et al. 2022

IEEE Commun. Surv. Tutorials

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Section: Energy-efficient Coolingmentioning

confidence: 99%

Toward a Systematic Survey for Carbon Neutral Data Centers

Cao

Zhou

et al. 2022

IEEE Commun. Surv. Tutorials

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“…Extreme seeking is an active and dynamic search approach, designed to seek for optimal system con guration even when the system is constantly in unstable state, e.g., [17,23]. However, existing results show that extreme seeking may not always nd the optimum in the search space, because of the dynamic nature of the chiller plant system.…”

Section: Related Workmentioning

confidence: 99%

Data Driven Chiller Plant Energy Optimization with Domain Knowledge

Chai

Keating

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simpli ed assumptions, over the equipments. Conventional optimization techniques using physical models make decisions of online parameter tuning, based on very limited information of hardware speci cations and external conditions, e.g., outdoor weather. In recent years, new generation of sensors is becoming essential part of new chiller plants, for the rst time allowing the system administrators to continuously monitor the running status of all equipments in a timely and accurate way. e explosive growth of data owing to databases, driven by the increasing analytical power by machine learning and data mining, unveils new possibilities of data-driven approaches for real-time chiller plant optimization.is paper presents our research and industrial experience on the adoption of data models and optimizations on chiller plant and discusses the lessons learnt from our practice on real world plants. Instead of employing complex machine learning models, we emphasize the incorporation of appropriate domain knowledge into data analysis tools, which turns out to be the key performance improver over state-of-the-art deep learning techniques by a signi cant margin. Our empirical *

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Real-time optimization of a chilled water plant with parallel chillers based on extremum seeking control

House

et al. 2017

Applied Energy

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Optimization and sequencing of chilled-water plant based on extremum seeking control

Cited by 7 publications

References 20 publications

Toward a Systematic Survey for Carbon Neutral Data Centers

Toward a Systematic Survey for Carbon Neutral Data Centers

Data Driven Chiller Plant Energy Optimization with Domain Knowledge

Real-time optimization of a chilled water plant with parallel chillers based on extremum seeking control

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