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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 李家岩(Chia-Yen Lee) | |
dc.contributor.author | Chih-Chun Chang | en |
dc.contributor.author | 張智鈞 | zh_TW |
dc.date.accessioned | 2023-03-19T22:16:45Z | - |
dc.date.copyright | 2022-10-14 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-09-19 | |
dc.identifier.citation | Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Chien, C.-F., Chen, Y.-J., Han, Y.-T., & Wu, Y.-C. (2021). Industry 3.5 for optimizing chiller configuration for energy saving and an empirical study for semiconductor manufacturing. Resources, Conservation and Recycling, 168, 105247. Chow, T., Zhang, G., Lin, Z., & Song, C. (2002). Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings, 34(1), 103-109. Evans, R., & Gao, J. (2016). Deepmind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 158. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. Gelfand, A. E. (2000). Gibbs sampling. Journal of the American statistical Association, 95(452), 1300-1304. Hung, Y.-H., Lee, C.-Y., Tsai, C.-H., & Lu, Y.-M. (2022). Constrained particle swarm optimization for health maintenance in three-mass resonant servo control system with LuGre friction model. Annals of Operations Research, 311(1), 131-150. Industrial Technology Research Institute. (2019). 109 ITRI household electricity consumption habits survey. Retrieved from https://energy-smartcity.energypark.org.tw/asset/upload/2020培力課程-弱勢能源關懷/3-低所得收入戶家電持有分析_縣市培力課程.pdf Lazrak, A., Boudehenn, F., Bonnot, S., Fraisse, G., Leconte, A., Papillon, P., & Souyri, B. (2016). Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation. Renewable energy, 86, 1009-1022. Lee, C.-Y., Chou, B.-J., & Huang, C.-F. (2022). Data science and reinforcement learning for price forecasting and raw material procurement in petrochemical industry. Advanced Engineering Informatics, 51, 101443. Liao, Y., & Huang, G. (2019). 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, 101616. Lowe, R., Wu, Y. I., Tamar, A., Harb, J., Pieter Abbeel, O., & Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in neural information processing systems, 30. Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. (2012). Ensemble approaches for regression: A survey. Acm computing surveys (csur), 45(1), 1-40. Nagarathinam, S., Menon, V., Vasan, A., & Sivasubramaniam, A. (2020). MARCO-Multi-Agent Reinforcement learning based COntrol of building HVAC systems. Paper presented at the Proceedings of the Eleventh ACM International Conference on Future Energy Systems. Nishiguchi, J., Konda, T., & Dazai, R. (2010). Data-driven optimal control for building energy conservation. Paper presented at the Proceedings of SICE Annual Conference 2010. Qiu, S., Li, Z., Li, Z., Li, J., Long, S., & Li, X. (2020). Model-free control method based on reinforcement learning for building cooling water systems: Validation by measured data-based simulation. Energy and Buildings, 218, 110055. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. Van Hasselt, H., Guez, A., & Silver, D. (2016). Deep reinforcement learning with double q-learning. Paper presented at the Proceedings of the AAAI conference on artificial intelligence. Vu, H. D., Chai, K. S., Keating, B., Tursynbek, N., Xu, B., Yang, K., . . . Zhang, Z. (2017). Data driven chiller plant energy optimization with domain knowledge. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Wang, L., Lee, E. W. M., Yuen, R. K., & Feng, W. (2019). Cooling load forecasting-based predictive optimisation for chiller plants. Energy and Buildings, 198, 261-274. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84590 | - |
dc.description.abstract | 隨著近年來智慧製造的趨勢,除了在生產線或排程上的最佳化,節能與環保的議題在製造現場也越來越受到重視,而在廠房的耗電量中,其中有一大部分的耗電量是來自冷氣機,工廠因為有大量的機台需要散熱,並且生產線又要維持在穩定的溫度,所以有大量的熱需要排出,工廠散熱使用的是冰水系統,由冰水主機、水塔、水泵等元件所組成,不同的元件其中又包含了不同的溫度控制點,本研究希望能透過即時控制多個溫度控制點,並考量其之間的交互作用與不同元件的關係,做到冰機節能最佳化。同時我們發現在做控制點設定時,冰機的狀態值的變化十分的重要,我們考慮控制點與狀態值之間的關係,建立了可以模擬冰機系統的元預測模型,選擇設定點後,模擬系統會更新冰機狀態直到穩定的狀態,最後再預測最終的效率值。建立完模擬模型後,我們使用強化學習去學習對應條件下最好的控制點動作,並且利用多智能體的機制,最佳化多點即時控制,達到節能的效果。本研究貢獻在於利用資料驅動模型來建立出複雜且多變的冰水系統,也說明了多智能體在多設定點下的幫助以及它不同的使用情境,最後我們的模擬模型不只可以用在冰水系統,更可以延伸到其他的多點控制系統,達到同樣的最佳化效果。 | zh_TW |
dc.description.abstract | With the trend of smart manufacturing in recent years, except for the optimization of the production lines or scheduling problems. Energy saving and environmental protection are also getting more and more attention in the manufacturing field. In the energy consumption of the factory, a large part of the energy consumption comes from the air conditioner. Because the factory has a large number of machines that need to dissipate heat, and the production line needs to maintain a stable temperature, it has a lot of cooling load. The factory uses a water-cooled chiller system for heat dissipation, which consists of the chiller, water tower, water pump, and other components. Different equipment includes multiple temperature setpoints. This study hopes to control the setpoints in real time and consider the interactive relationship between them and the energy consumption of different components in the system. Meanwhile, we found that the statuses of the chiller are also very important when setting the temperature setpoints. We consider the relationship between the setpoints and the statuses and construct the meta-prediction simulation model that can simulate the chiller system. After constructing the simulation model, we use reinforcement learning to learn the best setpoints action under the corresponding conditions and use the multi-agent technique to optimize the multi-point to achieve the effect of energy saving. The contribution of this research lies in the use of data-driven simulation models to build practical and complex chiller systems, and it also illustrates the help of multi-agents under multiple setpoints. Finally, our model can be used in chiller systems and other multi-point control systems and can be extended in the same way to achieve the effect of optimization. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T22:16:45Z (GMT). No. of bitstreams: 1 U0001-1609202215492400.pdf: 5193853 bytes, checksum: a9c2ba86b7df2508dc0ea64cd22994a4 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | List of Contents i List of Figures iv List of Tables vi Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Research Objective 2 1.3. Research Plan 3 Chapter 2 Literature Review 5 2.1. Chiller System Simulation 5 2.2. Chiller Sequencing Control Optimization 6 2.3. Chiller Setpoints Optimization 6 2.4. Reinforcement Learning 7 2.5. Comparing Our Research 8 Chapter 3 Framework and Methodology 11 3.1. Framework 11 3.2. Multi-machine Setpoint Conversion Module 14 3.3. Chiller Status Updating Module 15 3.4. Chiller Efficiency Prediction Module 18 3.5. Meta-prediction-based Simulation Environment 22 3.6. Multi-agent Reinforcement Learning 23 Chapter 4 Empirical Study 27 4.1 Water-cooled Chiller System Introduction 27 4.2 Empirical Case Introduction 29 4.3 Data Collection 30 4.4 Controlling Setpoints 31 4.5 Module Details 35 4.6 Module Results 36 4.7 Discussion 56 Chapter 5 Conclusion and Future Work 58 5.1 Conclusion 58 5.2 Research Contribution 59 5.3 Future Work 60 Appendix 63 A.1 Status with or without Meta-prediction 63 A.2 Grid Search in Meta-prediction-based Environment 65 Reference 67 About The Author 69 | |
dc.language.iso | en | |
dc.title | 基於多代理人與元預測之強化學習於冰機節能最佳化 | zh_TW |
dc.title | Multi-agent and Meta-prediction-based Reinforcement Learning for Energy Saving Optimization in Chiller System | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳建錦(Chien-Chin Chen),莊皓鈞(Hao-Chun Chuang),蔡炎龍(Yen-Lung Tsai) | |
dc.subject.keyword | 冰機最佳化,多控制點,元預測,模擬系統,強化學習,時間序列預測,多智能強化學習, | zh_TW |
dc.subject.keyword | Chiller system optimization,Multiple setpoints,Meta-prediction,Simulation system,Reinforcement learning,Time series forecasting,Multi-agent reinforcement learning, | en |
dc.relation.page | 69 | |
dc.identifier.doi | 10.6342/NTU202203474 | |
dc.rights.note | 同意授權(限校園內公開) | |
dc.date.accepted | 2022-09-21 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
dc.date.embargo-lift | 2025-09-21 | - |
顯示於系所單位: | 資訊管理學系 |
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