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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 詹瀅潔 | zh_TW |
| dc.contributor.advisor | Ying-Chieh Chan | en |
| dc.contributor.author | 林志璿 | zh_TW |
| dc.contributor.author | Zhi-Xuan Lin | en |
| dc.date.accessioned | 2024-06-17T16:08:54Z | - |
| dc.date.available | 2024-06-18 | - |
| dc.date.copyright | 2024-06-17 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-06-15 | - |
| dc.identifier.citation | [1] 台灣綠色生產力基金會. 辦公大樓節能技術手冊. 經濟部能源局, 2021.
[2] Xiupeng Wei, Guanglin Xu, and Andrew Kusiak. Modeling and optimization of a chiller plant. Energy, 73:898–907, 2014a. [3] Alessandro Beghi, Luca Cecchinato, and Mirco Rampazzo. A multi-phase genetic algorithm for the efficient management of multi-chiller systems. Energy Conversion and Management, 52(3):1650–1661, 2011. [4] Kuan-Chun Shih, Yih-Shiuan Chang, Ying-Chieh Chan, Yen-Chang Chen, and Chih Hsuan Lin. Quantifying uncertainties in cooling load predictions by using dropout as a bayesian approximation. 2022. [5] Gongsheng Huang, Yongjun Sun, and Peng Li. Fusion of redundant measurements for enhancing the reliability of total cooling load based chiller sequencing control. Automation in Construction, 20(7):789–798, 2011. [6] Yongjun Sun, Shengwei Wang, and Gongsheng Huang. Chiller sequencing control with enhanced robustness for energy efficient operation. Energy and Buildings, 41 (11):1246–1255, 2009. ISSN 0378-7788. doi: https://doi.org/10.1016/j.enbuild.2009.07.023. [7] Yundan Liao, Gongsheng Huang, Yunfei Ding, Huijun Wu, and Zhuangbo Feng. Robustness enhancement for chiller sequencing control under uncertainty. Applied Thermal Engineering, 141:811–818, 2018. ISSN 1359-4311. doi: https://doi.org/ 10.1016/j.applthermaleng.2018.06.031. [8] Yundan Liao et al. Uncertainty analysis for chiller sequencing control. Energy and Buildings, 85:187–198, 2014. [9] Sen Huang, Wangda Zuo, and Michael D Sohn. Amelioration of the cooling load based chiller sequencing control. Applied Energy, 168:204–215, 2016. [10] A. Jahanbani Ardakani, F. Fattahi Ardakani, and S.H. Hosseinian. A novel approach for optimal chiller loading using particle swarm optimization. Energy and Buildings, 40(12):2177–2187, 2008. ISSN 0378-7788. doi: https://doi.org/10.1016/j.enbuild. 2008.06.010. [11] Majid Karami and Liping Wang. Particle swarm optimization for control operation of an all-variable speed water-cooled chiller plant. Applied Thermal Engineering,130, 11 2017. doi: 10.1016/j.applthermaleng.2017.11.037. [12] Yung-Chung Chang. An outstanding method for saving energy-optimal chiller operation. IEEE Transactions on Energy Conversion, 21(2):527–532, 2006. doi:10.1109/TEC.2006.871358. [13] G Zames. Genetic algorithms in search,optimization and machine learning. Inf Tech J, 3(1):301, 1981. [14] Comparison of particle swarm optimization and genetic algorithm for facts-based controller design. Applied Soft Computing, 8(4):1418–1427, 2008. ISSN 1568-4946. doi: https://doi.org/10.1016/j.asoc.2007.10.009. Soft Computing for Dynamic Data Mining. [15] Lizhi Jia, Shen Wei, and Junjie Liu. A review of optimization approaches for controlling water-cooled central cooling systems. Building and Environment, 203:108100, 2021. ISSN 0360-1323. doi: https://doi.org/10.1016/j.buildenv.2021.108100. [16] Tien-Shun Chan, Yung-Chung Chang, and Jian-He Huang. Application of artificial neural network and genetic algorithm to the optimization of load distribution for a multiple-type-chiller plant. In Building Simulation, volume 10, pages 711–722. Springer, 2017. [17] Mirjalili and Seyedali Seyedali. Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, pages 43–55, 2019. [18] Kun Deng, Yu Sun, Sisi Li, Yan Lu, Jack Brouwer, Prashant G. Mehta, MengChu Zhou, and Amit Chakraborty. Model predictive control of central chiller plant with thermal energy storage via dynamic programming and mixed-integer linear program ming. IEEE Transactions on Automation Science and Engineering, 12(2):565–579, April 2015. ISSN 1558-3783. doi: 10.1109/TASE.2014.2352280. [19] Xiupeng Wei, Guanglin Xu, and Andrew Kusiak. Modeling and optimization of a chiller plant. Energy, 73:898–907, 2014b. ISSN 0360-5442. doi: https://doi.org/10.1016/j.energy.2014.06.102. [20] H. Yin, Z. Tang, and C. Yang. Predicting hourly electricity consumption of chillers in subway stations: A comparison of support vector machine and different artificial neural networks. Journal of Building Engineering, 76:107179, 2023. ISSN 2352-7102. https://doi.org/10.1016/j.jobe.2023.107179. [21] Kun He, Qiming Fu, You Lu, Yunzhe Wang, Jun Luo, Hongjie Wu, and Jianping Chen. Predictive control optimization of chiller plants based on deep reinforcement learning. Journal of Building Engineering, 76:107158, 2023. ISSN 2352-7102. https://doi.org/10.1016/j.jobe.2023.107158. [22] Sihao Chen, Puxian Ding, Guang Zhou, Xiaoqing Zhou, Jing Li, Liangzhu (Leon) Wang, Huijun Wu, Chengliang Fan, and Jiangbo Li. A novel machine learning based model predictive control framework for improving the energy efficiency of air-conditioning systems. Energy and Buildings, 294:113258, 2023. ISSN 0378-7788. doi:https://doi.org/10.1016/j.enbuild.2023.113258. [23] Yundan Liao, Yongjun Sun, and Gongsheng Huang. Robustness analysis of chillersequencing control. Energy Conversion and Management, 103:180–190, 2015. ISSN0196-8904.doi:https://doi.org/10.1016/j.enconman.2015.06.060. [24] Yudong Ma, Francesco Borrelli, Brandon Hencey, Brian Coffey, Sorin Bengea, andPhilip Haves. Model predictive control for the operation of building cooling systems.IEEE Transactions on Control Systems Technology, 20(3):796–803, 2012. doi:10.1109/TCST.2011.2124461. [25] Zhengwei Li, Gongsheng Huang, and Yongjun Sun. Stochastic chiller sequencingcontrol. Energy and Buildings, 84:203–213, 2014. [26] 詹添順. 應用類神經網路與基因演算法於混合型冰水主機群之負載分配最佳化.PhD thesis, 2017. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92735 | - |
| dc.description.abstract | 由於台灣位於亞熱帶的地區，屬於炎熱且潮濕的氣候，中央空調是大多數建築維持室內熱舒適度的必要工具。在一般使用下，空調耗電量會占整棟建築的40%，在尖峰時刻更會達到50% 。然而，當全球暖化效應下，全球平均溫度不斷上升，空調耗電量將會佔整棟建築的更大比例，減少空調的耗電量不僅能減緩溫室效應、降低發電廠負擔，更能減少電費支出。前人研究指出，不當操作冰水主機將會使整體建築能源消耗大幅提升，因此本研究期望透過基因演算法解決冰水主機負載最佳化(optimal chiller loading, OCL)與冰水主機時序控制最佳化 (optimal chiller sequencing, OCS)，同時符合現實操作的合理性，以達到比傳統人工操作更省電的結果。
本研究選取辦公大樓做為研究對象，主要針對改善負載分配效率、開關機次數與限制最大連續運轉時數提出新的方法。第一步取得歷史資料做為訓練值對未來空調需求量做預測，第二步將預測值設定為基因演算法的目標值，取得最佳的冰水主機時序控制(Sequencing Control):是指在多個主機間決定開關機，第三步將前一步的結果做為限制條件，以實際的歷史資料做為實際值設定為基因演算法的目標值，模擬實際發生的情形作為驗證。 研究結果顯示與傳統人工操作相比，採用基因演算法搭配預測的空調需求量做控制並考慮不確定性，能在年耗電量降低的情況，不額外增加切換次數與機械損耗，且易於應用於實際案例。與歷史資料相比，年耗電量降低約15%、切換次數減少約16%、COP提升約14%。 | zh_TW |
| dc.description.abstract | Due to its location in the subtropical region, Taiwan experiences a hot and humid climate, making central air conditioning a necessary tool for maintaining indoor thermal comfort in most buildings. Under normal usage, air conditioning accounts for 40% of the total electricity consumption of a building, reaching up to 50% during peak hours. However, with the ongoing global warming effect and the continuous increase in global average temperatures, air conditioning's electricity consumption will occupy a larger proportion of the building's total energy usage. Reducing air conditioning's power consumption not only helps mitigate the greenhouse effect and lessen the burden on power plants but also leads to reduced electricity expenses. Previous studies have indicated that improper operation of chiller plants can significantly increase the overall energy consumption of a building. Therefore, this study endeavors to utilize genetic algorithm to tackle the complex optimization challenges associated with optimal chiller loading (OCL) and optimal chiller sequencing (OCS) in order to achieve both practicality in real-world operations and greater energy efficiency compared to traditional manual operations.
Within this study, office building serve as the focal point for research, representing a crucial domain where efficient energy utilization is paramount. It primarily focuses on improving load distribution efficiency, reducing the number of on-off cycles, and limiting the maximum continuous operating hours by proposing a new method. The first step involves obtaining historical data as training values to predict future air conditioning demand. The second step sets the predicted values as the target values for the genetic algorithm, aiming to achieve the optimal sequencing control of the chiller plants, which refers to determining the on-off status among multiple units. The third step uses the results from the previous step as constraints and sets the actual historical data as the target values for the genetic algorithm, simulating real-life scenarios for validation. The research results demonstrate that compared to traditional manual operation, using genetic algorithms in combination with predicted air conditioning demand for control while considering uncertainty can reduce annual electricity consumption without increasing additional switching times and mechanical losses. Moreover, it is easily applicable to practical cases. Compared to historical data, the annual electricity consumption is reduced by approximately 15%, the number of switching times is reduced by around 16%, and the Coefficient of Performance(COP) is increased by approximately 14%. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-06-17T16:08:54Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-06-17T16:08:54Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 iii 摘要 v Abstract vii 目次 xi 圖次 xv 表次 xvii 符號列表 xix 第一章 緒論 1 1.1 研究背景 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 研究目的 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 第二章 文獻探討 5 2.1 冰水主機控制方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 最佳化演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 基因演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 模型預測控制 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.1 不確定性的分析方法 . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 文獻探討小結 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 第三章 研究方法 13 3.1 研究架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 問題描述 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 預測資料 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.3 目標函數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.4 限制條件 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.5 軟硬體工具 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 專家訪談 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.1 冰水主機保養頻率與耗時 . . . . . . . . . . . . . . . . . . . . . . 18 3.2.2 冰水主機休息機制決定 . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.3 人工操作與程式控制 . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.4 主機運轉切換週期 . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.5 切換次數的記錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.6 定期保養的重要性 . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.7 不當控制所造成的危害 . . . . . . . . . . . . . . . . . . . . . . . 21 3.2.8 小結 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 最佳化方法: 基因演算法 . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.1 染色體編碼 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.2 適應值函數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.3 主要運算元 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.4 多相數基因演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4 系統開發 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 資料清理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.1.1 DBSCAN . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.2 懲罰函數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4.2.1 切換次數懲罰 . . . . . . . . . . . . . . . . . . . . . . 33 3.4.2.2 邊界條件分析-死區控制與線性控制 . . . . . . . . . 33 3.5 變異機率 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.6 模型驗證 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.6.1 不確定性 (Uncertainty) . . . . . . . . . . . . . . . . . . . . . . . . 38 第四章 研究結果與討論 41 4.1 系統描述 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.1.1 實驗環境 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.1.2 案場資訊 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.1.3 冰水主機耗電方程式 . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 基因演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2.1 變異機率 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3 懲罰函數-參數分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.1 切換次數分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3.1.1 配置 A . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3.1.2 配置 B . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.1.3 小結 . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.2 邊界條件分析-線性控制 . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.2.1 配置 A 與配置 B . . . . . . . . . . . . . . . . . . . . 52 4.4 最大連續運轉時間 (Maximum Continuous Operation Time) . . . . . . 55 4.4.1 配置 A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.4.2 配置 B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.3 小結 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.5 實驗驗證與結果展示 . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.5.1 不同不確定性下之不足數分析 . . . . . . . . . . . . . . . . . . . 60 4.5.1.1 配置 A: 不足數 (Insufficient Numbers) . . . . . . . . 60 4.5.1.2 配置 B: 不足數 (Insufficient Numbers) . . . . . . . . . 62 4.5.2 耗電量 (Power Consumption) . . . . . . . . . . . . . . . . . . . . 64 4.6 研究結果分析與比較 . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.7 討論與建議 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 第五章 結論 73 參考文獻 75 附錄 A — 專家訪談內容 79 A.1 提問大綱 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 A.2 專家 A-訪談逐字稿 . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 A.2.1 專家 A-後續訪談 . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 A.3 專家 B-信件訪談 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 切換次數 | zh_TW |
| dc.subject | 不確定性 | zh_TW |
| dc.subject | 時序控制 | zh_TW |
| dc.subject | 冰水主機 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | 運轉時間 | zh_TW |
| dc.subject | 環境永續與節能 | zh_TW |
| dc.subject | Switching times | en |
| dc.subject | Genetic Algorithm | en |
| dc.subject | Chiller plant | en |
| dc.subject | Sequencing control | en |
| dc.subject | Uncertainty | en |
| dc.subject | operating time | en |
| dc.subject | Environmental Sustainability and Energy Conservation | en |
| dc.title | 應用預測資料與不確定性之冰水主機群負載分配最佳化 | zh_TW |
| dc.title | Optimization of chiller loading with Predictive Data and Uncertainty | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 謝依芸;謝宜桓 | zh_TW |
| dc.contributor.oralexamcommittee | I-Yun Lisa Hsieh;Yi-Huan Hsieh | en |
| dc.subject.keyword | 基因演算法,冰水主機,時序控制,不確定性,切換次數,運轉時間,環境永續與節能, | zh_TW |
| dc.subject.keyword | Genetic Algorithm,Chiller plant,Sequencing control,Uncertainty,Switching times,operating time,Environmental Sustainability and Energy Conservation, | en |
| dc.relation.page | 88 | - |
| dc.identifier.doi | 10.6342/NTU202304185 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-06-17 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2029-06-14 | - |
| 顯示於系所單位： | 土木工程學系 | |
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