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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4384 |
| Date | 10 August 2018 |
| Creators | Cox, Sam J |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
Page generated in 0.0023 seconds