預測能源轉型趨勢下電動車在建築能耗比例： 以台北市為例

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蘇于翔(Yu-Xiang Su) 查詢紙本館藏

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土木工程學系

論文名稱

預測能源轉型趨勢下電動車在建築能耗比例：以台北市為例
(Forecast Energy Transition Ratio of Electric Vehicle at Building Energy Consumption：A Case of Taipei City)

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摘要(中)

在2050年的淨零碳排政策，從全球的觀點探討碳排放量(Carbon Footprint)，人們主要以建築物為居住場所，根據國際能源總署( International Energy Agency, IEA )，提及在碳排部分建築部門占40 %、交通運輸部門占23% ，其餘的碳排產生由工業部門及製造部門等。
若電動車的趨勢不斷成長，每棟建築物在未來都將承擔充電樁所產生的電能，而燃油車會逐漸減少，即可以預測交通運輸部門的碳排會大幅度的減少，在國內公路運輸使用能耗高達96.8 %，其中小客車使用能耗約52.18 %，大貨車約16.62 %。換句話說，若未來每棟建築物都設立電動車的充電樁，使用能源的比例與交通及建築都保持不變的狀態，則我國會有將近50 %的能源使用在建築場域中，但根據各國所規定的新建建築需達到建築淨零耗能標準，此現象不但使城市的能源面臨巨大的挑戰也增加了淨零碳排的困難度。
本論文以台北市為例在老屋修繕、改建、及新建築設計階段以Revit Insight 基於BIM與BEM之間以EUI評估建築耗能並經過調整達到最佳效果且以深度學習預測未來電動車成長，從大格局的城市等級評析建築物的耗能或電力運輸的影響，隨著淨零碳排趨勢與進步，希望能夠藉此分析以提升人類的生活品質且達到永續節能的效果。

摘要(英)

According to the Net-Zero Carbon Emissions by 2050, from the global aspect of carbon footprint, the main use of construction is for residence. The International Energy Agency indicates that Construction section produces 40% ,Transportation section produces 23% ,Industry and Production section produce the rest of the carbon footprint.
With the development of electric vehicles, fossil fuel vehicles will eventually be replaced. It can be predicted that the carbon footprint from Transportation section will substantially decrease. Thus, Construction section will cover most of the energy consumption it. Domestic highway accounts for 96.8%, passenger car accounts for 52.18% and truck accounts for 16.62 percent of the energy consumption. In other words, if there are charging piles in every single building and the percentage of energy consumption from transportation and construction keep the same, almost 50% of the energy consumption in our country will be from Construction section. However, since many countries have regulated policy of Zero-energy buildings, cities are facing significant challenges of energy consumption and it increases the difficulties of zero carbon footprint.
This theses is using Taipei City as an example of predicting future growth of electric vehicles, the design, repair and renewal of construction. Using city-level energy consumption of construction and electrical transportation to evaluate, with the foundation of BIM and BEM such as Revit Insight, The method must be further verified and analyzed. With the progression of Zero Carbon Footprint, we hope that we can improve human beings’ living conditions to reach energy sustainability.

關鍵字(中)

★ 電動車
★ 深度學習
★ EUI
★ 建築資訊模型
★ 淨零碳排

關鍵字(英)

★ Electric vehicle
★ Deep learning
★ EUI
★ BIM
★ Net-Zero Carbon

論文目次

目錄
摘要 i
Abstract ii
誌謝 iv
目錄 v
圖目錄 viii
表目錄 x
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究問題與目的 3
1-3 研究範圍與限制 4
1-4 研究流程 5
1-5 論文架構 7
第二章文獻回顧 8
2-1 全球能源分析及我國能源轉型目標與願景 8
2-1-1 燃油車相關法規 11
2-1-2 國內加油站市場競爭與現況 12
2-1-3 國內加油站產業面臨的挑戰 13
2-2 電動車相關介紹種類 14
2-2-1 電動車的興起及充電設施的建置 15
2-2-2 電動車製造商 16
2-2-3 先進國家電動車成長與政策 18
2-3 人工智慧之相關文獻 20
2-3-1 機器學習 20
2-3-2 深度學習 21
2-4 建築能耗模型與建築資訊模型相關文獻 23
2-4-1 Building Information Modeling 24
2-4-2 Building Energy Modeling 26
2-4-3 Energy plus 28
2-4-4 Energy Use Intensity 31
2-4-5 Autodesk Insight 33
2-5 文獻回顧總結 35
第三章電動車轉換效率與成長量預測 36
3-1 能量轉換效率 36
3-1-1 燃油車推估 40
3-1-2 電動車推估 44
3-2 全球電動車預測成長量 47
3-2-1 台灣電動車與能源轉型成長預估 51
3-2-2 台北市能源轉型先行城市之電動車成長預估 52
3-2-3 電動車成長對城市各項能源基礎設施造成的影響 54
第四章 Revit建築能源評析 55
4-1 Revit能源分析設定 55
4-1-1 模型前置設定 56
4-1-2 模型能源分析設置 58
4-1-3 Insight 分析及視覺化 62
4-2 先行城市實際操作 67
4-2-1 住戶用電分析 69
4-2-2 住戶含公設用電分析 77
4-2-3 建築物與電動樁結果分析 80
第五章結論與建議 81
5-1 結論 81
5-2 建議 82
5-3 貢獻 82
參考文獻 83
附錄 A 93
評審意見回覆表 99

參考文獻

洪仲緯, (2021). 電動車電池電量的動態平滑估計, 碩士論文, 國立中山大學電機工程研究所, 高雄市, 臺灣。

黃文佑, (2020). 液流電池應用於電動車輛之研究, 碩士論文, 國立臺北科技大學車輛工程研究所, 台北市, 臺灣

呂依庭, (2021). 能源轉型下的能源正義策略:以英國電力零售市場的脆弱消費者為例, 碩士論文, 國立台灣大學理學院氣候變遷與永續發展所, 台北市, 臺灣

李浩銓, 林素琴, & 杜威達. (2013). 我國住宅部門能源耗用調查與應用分析. 冷凍空調與能源科技雜誌, (82), pp.30-36.

陳恩仕, 蔡明瑞, & 蔡丁貴.(2015). 臺南區商辦建物之窗牆比與窗玻特性對整體節能效率的影響.台灣能源期刊,(3),pp.345-356.

徐虎嘯, 呂文弘, 王家瑩, 嚴佳茹, 潘振宇, 王育忠.(2019).「既有建築綠建築評估手冊之研究」.創新循環綠建築環境科技計畫(一)協同研究計畫第3案.

何信志，2011，集合住宅耗能與節能潛力，臺灣綠色生產力基金會簡報

工業技術研究院, (2021). 世界能源展望報告,台灣

Aljundi, K., Pinto, A., & Rodrigues, F. (2016). Energy analysis using cooperation between bim tools (Revit and Green Building Studio) and Energy Plus. In Actas del 1º Congresso Português de Building Information Modelling.

Alves Ribeiro Rosa, J. G., Armellini, F., & Robert, J. M. (2021). Capturing Future Trends in Customer Needs for the Design of Next-Generation Gas Station Services. In Congress of the International Ergonomics Association pp. 781-787, Springer, Cham.

Asekomeh, A., Gershon, O., & Azubuike, S. I. (2021). Optimally Clocking the Low Carbon Energy Mile to Achieve the Sustainable Development Goals: Evidence from Dundee′s Electric Vehicle Strategy. Energies, 14(4), pp. 842.

Axsen, J., & Kurani, K. S. (2012). Who can recharge a plug-in electric vehicle at home? Transportation Research Part D: Transport and Environment, 17(5), pp. 349-353.

Azhar, S. (2011). Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and management in engineering, 11(3), pp. 241-252.
Bazjanac, V. (2008). IFC BIM-based methodology for semi-automated building energy performance simulation (No. LBNL-919E). Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States).

Buckley, N., Mills, G., Reinhart, C., & Berzolla, Z. M. (2021). Using urban building energy modelling (UBEM) to support the new European Union’s Green Deal: Case study of Dublin Ireland. Energy and Buildings, 247, 111115.

Cai, H., Jia, X., Chiu, A. S., Hu, X., & Xu, M. (2014). Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet. Transportation Research Part D: Transport and Environment, 33, pp. 39-46.

Chan, C. C. (2002). The state of the art of electric and hybrid vehicles. Proceedings of the IEEE, 90(2) , pp. 247-275.

Chen, Y., Guo, M., Chen, Z., Chen, Z., & Ji, Y. (2022). Physical energy and data-driven models in building energy prediction: A review. Energy Reports, 8, 2656-2671.

Crawley, D. B., Lawrie, L. K., Pedersen, C. O., & Winkelmann, F. C. (2000). Energy plus: energy simulation program. Journal of ASHRAE , 42(4), pp. 49-56.

Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Pedersen, C. O., ... & Glazer, J. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and buildings, 33(4), pp. 319-331.
Das, H. S., Rahman, M. M., Li, S., & Tan, C. W. (2020). Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renewable and Sustainable Energy Reviews, 120, 109618.

Eisel, M., Schmidt, J., & Kolbe, L. M. (2014). Finding suitable locations for charging stations. IEEE International Electric Vehicle Conference (IEVC). IEEE, Dec 1-8, 2014

Fernández, R. A. (2021). Stochastic analysis of future scenarios for battery electric vehicle deployment and the upgrade of the electricity generation system in Spain. Journal of Cleaner Production, 316, 128101.

Hasan, M. R., Islam, M. S., & Akter, J. (2017). Energy Performance Analysis of an Office Building Using BIM: A Case Study. Journal of System and Management Sciences, 7(3), pp. 34-53.

Hawksworth, J., & Cookson, G. (2006). The world in 2050. How big will the major emerging market economies get and how can the OECD compete.

Hetherington, R., Laney, R., Peake, S., & Oldham, D. (2011). Integrated building design, information and simulation modelling: the need for a new hierarchy.

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.

Ilhan, B., & Yaman, H. (2016). Green building assessment tool (GBAT) for integrated BIM-based design decisions. Automation in Construction, 70, pp. 26-37.

IEA (2017), Global EV Outlook 2017, IEA, Paris , 2017

IEA (2018), Global EV Outlook 2018, IEA, Paris, 2018

IEA (2019), Global EV Outlook 2019, IEA, Paris, 2019

IEA (2020), Global EV Outlook 2020, IEA, Paris, 2020

IEA (2021), Global EV Outlook 2021, IEA, Paris, 2021

Kareem, F. M., Abd, A. M., & Zehawi, R. N. (2021). Utilize BIM Technology for Achiveing Sustainable Passengers Terminal in Baghdad International Airport. Diyala Journal of Engineering Sciences, 14(4), pp. 62-78.

Kazado, D., Kavgic, M., & Eskicioglu, R. (2019). Integrating building information modeling (BIM) and sensor technology for facility management. Journal of Information Technology in Construction (ITcon), 24(23), pp. 440-458.

Kong, W., Luo, Y., Feng, G., Li, K., & Peng, H. (2019). Optimal location planning method of fast charging station for electric vehicles considering operators, drivers, vehicles, traffic flow and power grid. Energy, 186, 115826.

Kota, S., Haberl, J. S., Clayton, M. J., & Yan, W. (2014). Building Information Modeling (BIM)-based daylighting simulation and analysis. Energy and buildings, 81, pp. 391-403.

Krause, F., Bossel, H., & Müller-Reißmann, K. F. (1980). Energiewende–Wachstum und Wohlstand ohne Erdöl und Uran. [Energy Transition. Growth and Prosperity without Oil and Uranium] Frankfurt. Germany: Fischer.

Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2021). Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Applied Nanoscience, pp. 1-13.

Mahiwal, S. G., Bhoi, M. K., & Bhatt, N. (2021). Evaluation of energy use intensity (EUI) and energy cost of commercial building in India using BIM technology. Asian Journal of Civil Engineering, 22(5), pp. 877-894.

Melo, S., Baptista, P., & Costa, Á. (2014). Comparing the use of small sized electric vehicles with diesel vans on city logistics. Procedia-Social and Behavioral Sciences, 111, pp. 1265-1274.

Pérez-Lombard, L., Ortiz, J., González, R., & Maestre, I. R. (2009). A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes. Energy and Buildings, 41(3), pp. 272-278

Pevec, D., Babic, J., Carvalho, A., Ghiassi-Farrokhfal, Y., Ketter, W., & Podobnik, V. (2019). Electric vehicle range anxiety: an obstacle for the personal transportation (r) evolution? International conference on smart and sustainable technologies . (pp. 1-8). IEEE, Splitech June 4, 2019.

Rajashekara, K. (1994). History of electric vehicles in General Motors. IEEE transactions on industry applications, 30(4) , pp. 897-904.
Ramdhani, R. D., Baga, L. M., & Nurhayati, P. (2021, July). Read the Future Needs of Gas Station Consumers in DKI Jakarta And Banten. In Business Innovation and Engineering Conference 2020 (BIEC 2020) (pp. 26-29). Atlantis Press.

Searle, J. R. (1980). Minds, brains, and programs. Behavioral and brain sciences, 3(3) , pp. 417-424.

Seghier, T. E., Lim, Y. W., Ahmad, M. H., & Samuel, W. O. (2017). Building envelope thermal performance assessment using visual programming and BIM, based on ETTV requirement of Green Mark and GreenRE. International Journal of built environment and sustainability, 4(3).

Smith, D. K., & Tardif, M. (2009). Building information modeling: a strategic implementation guide for architects, engineers, constructors, and real estate asset managers. John Wiley & Sons.

Spiridigliozzi, G., Pompei, L., Cornaro, C., De Santoli, L., & Bisegna, F. (2019, September). BIM-BEM support tools for early stages of zero-energy building design. In IOP Conference Series: Materials Science and Engineering (Vol. 609, No. 7, p. 072075). IOP Publishing.

Stewart, M. (2019). The actual difference between statistics and machine learning. Towards Data Science, 24(3) , pp. 19.

Trčka, M., & Hensen, J. L. (2010). Overview of HVAC system simulation. Automation in Construction, 19(2) , pp. 93-99.

Tyagi, V., Dwivedi, P., & Gupta, A. (2019). Solar Rooftop PV Systems-Markets, Policies and Future Potential. Int. J. Renew. Energy Res.(IJRER), 3(6) , pp. 1686-1691.

Uimonen, S., & Lehtonen, M. (2020). Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data. Energies, 13(21), 5700.

Zanni, M. A., Soetanto, R., & Ruikar, K. (2017). Towards a BIM-enabled sustainable building design process: roles, responsibilities, and requirements. Architectural engineering and design management, 13(2) , pp. 101-129.

指導教授

周建成(Chien-Cheng Chou)

審核日期

2022-7-25

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