| 논문명 |
가우시안 프로세스 모델과 냉동기 실시간 최적 제어 / Gaussian Process Model for Real-Time Optimal Control of Chiller System |
| 저자명 |
김영진 (Kim, Young-Jin) ; 박철수(ParkCheol-Soo) |
| 수록사항 |
대한건축학회논문집 계획계, v.30 n.7 (2014-07) |
| 주제어 |
모델기반 예측제어 ; 인버스 모델 ; 가우시안 프로세스 ; 베이지안 접근 ; 유전자 알고리즘 ; Model Predictive Control(MPC) ; Inverse model ; Gaussian Process ; Bayesian approach ; Genetic Algorithm |
| 요약2 |
For Model-Predictive Control (MPC) to be implemented in real application, data driven inverse models are advantageous since they are easily constructed as well as relatively fast and accurate, compared to first principle based models (simplified calculation [ISO 13790], dynamic simulation [EnergyPlus, ESP-r, TRNSYS, etc.], state space models, etc.). Gaussian Process Model (GPM), one of the inverse methods, can be beneficially used for real time stochastic optimal control of nonlinear building systems, since the GPM consumes much less computational time and does not require significant efforts. The GPM is a black-box model based on Bayesian approach based on measured in-output dataset. For real-time optimal control of chiller operation, this paper presents a coupling between the GPM and an optimization routine in MATLAB optimization toolbox. The two control parameters studied in the paper are the outlet temperatures of chilled water loop and cooling tower loop. In particular, Genetic Algorithm (GA), one of the meta-heuristic methods, was applied to find optimal control strategy. It is elaborated in the paper that GPM produces reliable control results reflecting probabilistic natures of the chiller system. |
