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???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
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dc.contributor.advisor | 張斐章(Fi-John Chang) | |
dc.contributor.author | Chia-Hui Hsu | en |
dc.contributor.author | 徐嘉徽 | zh_TW |
dc.date.accessioned | 2021-06-15T12:32:18Z | - |
dc.date.available | 2020-08-24 | |
dc.date.copyright | 2020-08-24 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-18 | |
dc.identifier.citation | 1. Al-Mezeini, N. K., Oukil, A., Al-Ismaili, A. M. (2020). Investigating the efficiency of greenhouse production in Oman: A two-stage approach based on Data Envelopment Analysis and double bootstrapping. Journal of Cleaner Production, 247, 119160.
2. Ammar, M. E., Davies, E. G. (2019). On the accuracy of crop production and water requirement calculations: Process-based crop modeling at daily, semi-weekly, and weekly time steps for integrated assessments. Journal of environmental management, 238, 460-472. 3. Chang, L. C., Shen, H. Y., Chang, F. J. (2014). Regional flood inundation nowcast using hybrid SOM and dynamic neural networks. Journal of Hydrology, 519, 476-489. 4. Chen, J., Yang, J., Zhao, J., Xu, F., Shen, Z., Zhang, L. (2016). Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method. Neurocomputing, 174, 1087-1100. 5. Choab, N., Allouhi, A., El Maakoul, A., Kousksou, T., Saadeddine, S., Jamil, A. (2019). Review on greenhouse microclimate and application: Design parameters, thermal modeling and simulation, climate controlling technologies. Solar Energy, 191, 109-137. 6. Esmaeli, H., Roshandel, R. (2020). Optimal design for solar greenhouses based on climate conditions. Renewable Energy, 145, 1255-1265. 7. Fox, J. A., Adriaanse, P., Stacey, N. T. (2019). greenhouse energy management: The thermal interaction of greenhouses with the ground. Journal of Cleaner Production, 235, 288-296. 8. Golzar, F., Heeren, N., Hellweg, S., Roshandel, R. (2018). A novel integrated framework to evaluate greenhouse energy demand and crop yield production. Renewable and Sustainable Energy Reviews, 96, 487-501. 9. Graamans, L., Baeza, E., Van Den Dobbelsteen, A., Tsafaras, I., Stanghellini, C. (2018). Plant factories versus greenhouses: Comparison of resource use efficiency. Agricultural Systems, 160, 31-43. 10. Joudi, K. A., Farhan, A. A. (2015). A dynamic model and an experimental study for the internal air and soil temperatures in an innovative greenhouse. Energy Conversion and Management, 91, 76-82. 11. Kingma, D. P., Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 12. Lee, C. S., Hoes, P., Cóstola, D., Hensen, J. L. M. (2019). Assessing the performance potential of climate adaptive greenhouse shells. Energy, 175, 534-545. 13. Lee, S. Y., Lee, I. B., Yeo, U. H., Kim, R. W., Kim, J. G. (2019). Optimal sensor placement for monitoring and controlling greenhouse internal environments. Biosystems Engineering, 188, 190-206. 14. Lin, D., Zhang, L., Xia, X. (2020). Hierarchical model predictive control of Venlo-type greenhouse climate for improving energy efficiency and reducing operating cost. Journal of Cleaner Production, 121513. 15. Liu, H., Li, H., Ning, H., Zhang, X., Li, S., Pang, J., ... Sun, J. (2019). Optimizing irrigation frequency and amount to balance yield, fruit quality and water use efficiency of greenhouse tomato. Agricultural Water Management, 226, 105787. 16. Ma, D., Carpenter, N., Maki, H., Rehman, T. U., Tuinstra, M. R., Jin, J. (2019). Greenhouse environment modeling and simulation for microclimate control. Computers and Electronics in Agriculture, 162, 134-142. 17. Mannan, M., Al-Ansari, T., Mackey, H. R., Al-Ghamdi, S. G. (2018). Quantifying the energy, water and food nexus: A review of the latest developments based on life-cycle assessment. Journal of Cleaner Production, 193, 300-314. 18. Salmoral, G., Yan, X. (2018). Food-energy-water nexus: A life cycle analysis on virtual water and embodied energy in food consumption in the Tamar catchment, UK. Resources, Conservation and Recycling, 133, 320-330. 19. Sethi, V. P. (2009). On the selection of shape and orientation of a greenhouse: Thermal modeling and experimental validation. Solar Energy, 83(1), 21-38. 20. Van Beveren, P. J. M., Bontsema, J., Van Straten, G., Van Henten, E. J. (2015). Minimal heating and cooling in a modern rose greenhouse. Applied energy, 137, 97-109. 21. Vanthoor, B. H. E., De Visser, P. H. B., Stanghellini, C., Van Henten, E. J. (2011). A methodology for model-based greenhouse design: Part 2, description and validation of a tomato yield model. Biosystems engineering, 110(4), 378-395. 22. Vanthoor, B. H. E., Stanghellini, C., Van Henten, E. J., De Visser, P. H. B. (2011). A methodology for model-based greenhouse design: Part 1, a greenhouse climate model for a broad range of designs and climates. Biosystems Engineering, 110(4), 363-377. 23. Vanthoor, B. H. E., Van Henten, E. J., Stanghellini, C., De Visser, P. H. B. (2011). A methodology for model-based greenhouse design: Part 3, sensitivity analysis of a combined greenhouse climate-crop yield model. Biosystems engineering, 110(4), 396-412. 24. Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569-582. 25. Yu, H., Chen, Y., Hassan, S. G., Li, D. (2016). Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO. Computers and Electronics in Agriculture, 122, 94-102. 26. Zhang, X., Vesselinov, V. V. (2017). Integrated modeling approach for optimal management of water, energy and food security nexus. Advances in Water Resources, 101, 1-10. 27. Zhou, Y., Chang, F. J., Chang, L. C., Lee, W. D., Huang, A., Xu, C. Y., Guo, S. (2020). An advanced complementary scheme of floating photovoltaic and hydropower generation flourishing water-food-energy nexus synergies. Applied Energy, 275, 115389. 28. 艾民, 劉振奎, 楊延杰, 何莉莉. (2005). 溫度, 光照強度和 CO2 濃度對黃瓜葉片淨光合速率的影響. 瀋陽農業大學學報, 36(4), 414-418. 29. 張斐章、張麗秋,2015,類神經網路導論原理與應用,滄海書局。 30. 趙玉萍, 鄒志榮, 白鵬威, 任雷, 李鵬飛. (2010). 不同溫度對溫室番茄生長發育及產量的影響. 西北農業學報, 19(2), 133-137. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50198 | - |
dc.description.abstract | 維持穩定之作物產量是溫室耕種的主要優勢,然而為控制溫室內部環境,相較於傳統的露地栽培亦會消耗額外的資源;以水-能源-糧食鏈結(Water-Energy-Food Nexus, WEF Nexus)之管理為出發點,首要工作是建立一套能評估並最佳化溫室作物產量及資源消耗之整合性方法;由於溫室之作物產量在其內部環境受到妥善控制的前提下是可預期的,如何盡可能地減少控制過程中所消耗之水資源及能源是主要的探討重點。
為達成此目的,本次研究首先建立了機器學習模式以預測種植溫室之內部環境,包括氣溫、相對濕度,以及土壤含水量;並根據適宜作物生長之環境標準,若判斷預測結果超出此一標準,則會啟動環境之控制機制。針對判斷需啟動控制機制的情況,本次研究另外建立一評估模式決定適合之控制方式及其控制幅度,以調節溫室環境至符合環境標準之範圍。本次之研究區域為一位於彰化縣,種植小黃瓜及番茄之溫室,通過物聯網設備收集2019/01/08至2019/11/12之環境監測資料,資料解析度為10分鐘一筆,共計44304筆資料。針對溫室氣溫的部分,以30℃作為上限,並以外遮蔭作為控制手段時,結果顯示相較於「未來第一小時之氣溫預測值在30℃以上」,使用「當時刻氣溫觀測值在27℃以上」作為操作標準在低程度,即遮蔽面積較少之方案的選擇次數上明顯較前者多,顯示通過預測模式提前對未來之高溫情況作出應對能有效提升溫室控制內部氣溫之效率。 | zh_TW |
dc.description.abstract | Maintaining stable crop production is the main advantage of greenhouses. However, it would also consume additional resources to control its indoor environment, as compared to traditional open-field cultivation. In consideration of Water-Food-Energy Nexus (WFE Nexus) management, it’s essential to build an integrated methodology to estimate and optimize the production efficiency of greenhouses. Since the production of greenhouses is predictable if the indoor environment is well controlled, the main issue we should think about is how to reduce the consumption of resources as much as possible during the control process for greenhouse indoor environment.
For this purpose, we first build a machine learning-based model to predict indoor environment condition, including air temperature, relative humidity (RH), and soil volume water content (VWC), for the study greenhouse. Then according to the suitability criteria of the crop, the predicted values are utilized for environmental control if the values violate the requirements. Under such conditions, an estimation model is built to decide appropriate type and degree of control mechanisms for meeting the criteria to maintain stable crop production. The study area is a greenhouse located at the farm in Changhua County, Taiwan, cultivating cucumber and tomato. A total of 44,304 datasets were recorded by Internet of Things (IoT) from 2019/01/08 to 2019/11/12 at a 10-minute temporal resolution. For greenhouse indoor temperature control, we use 30℃ as the upper limit and shade cloth as the control method. The results show the operation efficiency is better with the prediction model than just utilizing real-time observation value for environmental control. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T12:32:18Z (GMT). No. of bitstreams: 1 U0001-1108202014560600.pdf: 5588556 bytes, checksum: f275ae26537ffb503cff50e26bb6f095 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 謝誌 I 摘要 III Abstract IV 目錄 V 圖目錄 VI 表目錄 VII 第1章 前言 1 1.1 研究緣起 1 1.2 研究目的 2 第2章 文獻回顧 3 2.1水-能源-糧食鏈結之相關研究 3 2.2溫室環境預測與控制之相關研究 3 2.3溫室資源消耗與效益評估之相關研究 5 第3章 理論概述 6 3.1 溫室預測及控制評估系統 6 3.2 內部環境預測模式 7 3.2.1 類神經網路概述 8 3.2.2 倒傳遞類神經網路(BPNN) 10 3.3 控制選擇與評估模式 13 3.3.1 模式架構 13 3.3.2 控制方案對溫室氣溫影響之模擬 14 第4章 研究案例 17 4.1 研究區域 17 4.2 資料收集 18 4.3 研究流程 19 4.4 評估指標 21 第5章 結果與討論 23 5.1 溫室內部環境之預測 23 5.1.1全因子輸入(模式1) 23 5.1.2通過決定係數進行因子篩選(模式2) 25 5.2 控制方案對溫室氣溫之影響模擬 28 5.2.1室外氣溫及室內光合有效輻射之預測模式 28 5.2.2室內氣溫模擬模式 31 5.3 探討不同操作標準對控制方案選擇之影響 48 5.3.1小黃瓜種植期間之操作結果 49 5.3.2番茄種植期間之操作結果 51 第6章 結論與建議 54 6.1 結論 54 6.2 建議 55 Reference 56 Appendix 60 a. 室外氣溫及室內光合有效輻射預測模式之測試結果 60 b. 各時段之k_ir分類 66 c. 小黃瓜及番茄種植期間之操作結果 68 | |
dc.language.iso | zh-TW | |
dc.title | 使用機器學習技術預測溫室室內環境以實現溫室環境之自動控制 | zh_TW |
dc.title | Predict indoor environment of greenhouses for automatic greenhouse environmental control using machine learning techniques | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張麗秋(Li-Chiu Chang),胡明哲(Ming-Che Hu),鄭舒婷(Su-Ting Cheng) | |
dc.subject.keyword | 水-能源-糧食鏈結,溫室,機器學習,物聯網, | zh_TW |
dc.subject.keyword | Water-Food-Energy Nexus (WFE Nexus),Greenhouse,Machine learning,Internet of Things (IoT), | en |
dc.relation.page | 75 | |
dc.identifier.doi | 10.6342/NTU202002954 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-19 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
Appears in Collections: | 生物環境系統工程學系 |
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