Theses and Dissertations

Sam J. Cox

Issuing Body

Mississippi State University

Advisor

Cho, Heejin

Committee Member

Mago, Pedro

Committee Member

Luck, Rogelio

Date of Degree

8-10-2018

Document Type

Graduate Thesis - Open Access

Major

Mechanical Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Mechanical Engineering

Abstract

Thermal energy storage can offer significant cost savings with time varying pricing. This study examines the effectiveness of using neural networks to model a district cooling system with ice storage for optimal control. Neural networks offer a fast performance estimation of a district cooling system with external inputs. A physics based model of the district cooling system is first developed to act as a virtual plant for the controller to communicate system states, in real time. Next, the neural network modeling the plant is developed and trained. This model is optimized using a genetic algorithm due to the on/off controls. Finally, a thermal load prediction algorithm is integrated to test under weather forecasts. It is shown through a case study that the optimal control scheme can effectively adapt to varying loads and varying prices to effectively reduce operating costs of the district cooling network by 16% for time of use pricing and 13% under real time pricing.

URI

https://hdl.handle.net/11668/19973

Comments

thermalload prediction||modelpredictivecontrol||optimalcontrol||districtcoolingsystem

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