최소가동시간 제약을 고려한 심층 강화학습 기반의 다중 냉동기 운영 모델
A Multi-chiller Operation Model Based on Deep Reinforcement Learning Considering Minimum Up-time Constraint
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
여름철 냉동기가 건물의 주 에너지 소비자로 고려됨에 따라 효율적인 냉동기 운영은 매우 중요한 문제로 고려된다. 그러나, 건물의 냉방수요가 건물 내외부 환경, 건물 재실자의 행동 등의 여러 요인에 의해 변동하고냉동기의 가동제약조건으로 인해 현재 시점의 운영이 미래 시점의 운영에 제약을 발생시킴에 따라 건물의냉방수요를 정확하게 만족하도록 냉동기를 운영하는 것은 어렵다. 본 연구에서는 이러한 문제를 해결하기 위해 냉동기의 최소가동시간을 고려한 심층 강화학습 기반의 다중 냉동기 운영 모델을 제안한다. 제안한 모델은 외기 정보와 냉방시스템 내부 정보로 구성된 상태에 따른 냉동기 운영 조합이 갖는 가치를 학습하고 실현 가능한 냉동기 운영 중 건물의 냉방수요와 냉동기에 의한 공급 부하 간의 차이를 최소화할 수 있는냉동기 운영 조합을 결정한다. 냉동기의 최소가동시간 제약을 고려한 훈련 알고리즘을 적용하여 제안한 모델의 현실 적용 가능성을 높였으며 실제 국내 A대학교의 데이터를 바탕으로 실험한 결과, 제안한 다중냉동기 운영 모델이 최소가동시간을 준수함과 동시에 건물 냉방 부하와의 차이 측면에서 A대학교의 기존냉동기 운영 로직보다 우수한 성능을 보임을 확인하였다.
Abstract
In summer, as chillers are considered the main energy consumer of building, the efficient chiller operation is considered important. However, it is difficult to operate chillers to meet the cooling demand of the building as the demand fluctuates with various factors like the internal, external environment and behavior of the occupants and as chiller’s constraint cause the current operation constrains operation in future. To address these problems, this study proposes a multi-chiller operation model based on deep reinforcement learning considering the minimum up-time of the chiller. The proposed model learns the value of the chiller operations according to the state composed of metrological and cooling system information and determines operation that minimizes the difference between the supply load and the cooling demand among feasible operations. The practical applicability was improved by applying the training algorithm considering the minimum up-time constraint and Experiments results using the actual data from a Korean university confirmed that the proposed model complies with the chiller constraints and outperforms the existing chiller operation logic of the university in terms of differences from the building cooling demand.
참고문헌 (23)
- Beghi, A., "A PSO-based algorithm for optimal multiple chiller systems operation", Applied Thermal Engineering, 32, pp.31~40 (2012)
- Chang, Y. C., "An outstanding method for saving energy-optimal chiller operation", IEEE transactions on energy conversion, 21(2), pp.527~532 (2006)
- dos Santos Coelho, L., "Optimal chiller loading for energy conservation using a new differential cuckoo search approach", Energy, 75, pp.237~243 (2014)
- Du, Y., "Demonstration of intelligent HVAC load management with deep reinforcement learning:real-world experience of machine learning in demand control", IEEE Power and Energy Magazine, 20(3), pp.42~53 (2022)
- He, K., "Predictive control optimization of chiller plants based on deep reinforcement learning", Journal of Building Engineering, 76, pp.107158~ (2023)
- Henderson, P., "Deep reinforcement learning that matters", Proceedings of the AAAI conference on artificial intelligence, 32(1) (2018)
- Hyun, S. H., "Analysis of uncertainty in natural ventilation predictions of high-rise apartment buildings", Building Services Engineering Research and Technology, 29(4), pp.311~326 (2008)
- Jia, W., "DQN Algorithm Based on Target Value Network Parameter Dynamic Update", IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET), pp.285~289 (2021)
- Kwok, S. S., "An intelligent approach to assessing the effect of building occupancy on building cooling load prediction", Building and Environment, 46(8), pp.1681~1690 (2011)
- Kwon, Y. D., "Pomo: Policy optimization with multiple optima for reinforcement learning", Advances in Neural Information Processing Systems (NeurIPS), 33, pp.21188~21198 (2020)
- Lewis, F. L., "Reinforcement learning and adaptive dynamic programming for feedback control", IEEE circuits and systems magazine, 9(3), pp.32~50 (2009)
- Liao, Y., "A hybrid predictive sequencing control for multi-chiller plant with considerations of indoor environment control, energy conservation and economical operation cost", Sustainable Cities and Society, 49, pp.101616~ (2019)
- Liu, R., "The effects of memory replay in reinforcement learning", 2018 56th annual allerton conference on communication, control, and computing (Allerton), pp.478~485 (2018), IEEE
- Liu, Z., "Optimal chiller sequencing control in an office building considering the variation of chiller maximum cooling capacity", Energy and Buildings, 140, pp.430~442 (2017)
- Mavrovouniotis, M., "A survey of swarm intelligence for dynamic optimization: Algorithms and applications", Swarm and Evolutionary Computation, 33, pp.1~17 (2017)
- Nair, V., "Rectified linear units improve restricted boltzmann machines", Proceedings of the 27th international conference on machine learning (ICML-10), pp.807~814 (2010)
- Sala-Cardoso, E., "Predictive chiller operation: A data-driven loading and scheduling approach", Energy and Buildings, 208, pp.109639~ (2020)
- Ye, Y., "A multi-layer optimal chiller operation management framework", IEEE Industry Applications Society Annual Meeting (IAS), pp.1~6 (2016)
- Zhuang, C., "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties", Applied Energy, 280, pp.115983~ (2020)