在已開發國家中建築物的能源消耗量為能源使用總量的四分之一以上, 且於歐盟與美國的建築物能源消耗量已高於工業與運輸業的能源消耗量, 故建築物的能源效率已成為一個全球性的能源策略研究課題。基於一個常用的能耗模擬軟EnergyPlus 的缺點—輸入參數多且耗時, 本研究以Energyplus 為基礎提出一個「簡化的模擬流程」以估計建築物(包含商業與住宅等用途) 的能源消耗量。本研究所提出簡化模擬流程是以一個已存在的建築物模型(稱為Benchmark Model)為基礎, 修改其中關鍵的輸入參數如建築面積、材料結構、排程、天氣文件、窗牆比和屋簷長度。而所修改的模型可使用繪圖軟體SketchUp 來幫助呈現。在本研究中, 我們透過例子說明所提出的簡化模擬過程如何容易地應用於探討不同尺寸的屋簷對於建築物能源消耗的影響。結果顯示,屋簷長度對於中等規模的建築物是一個顯著的因子, 且屋簷長度為0.25米時成本效益最佳。

The energy consumption of buildings in developed countries comprises more than one quarter of total energy use in their countries. In the EU and the US, buildings consume more energy than industry or transport . As a result, energy efficiency in buildings has become a global research topic for energy policy. Motivated by the time-consuming disadvantages of EnergyPlus, a commonly used energy simulation software, this study proposes a ``simplified simulation process” based on EnergyPlus to estimate energy consumption for buildings, both residential and commercial. The proposed simplified simulation process is based on revising an existing building model, named the “benchmark model,” in terms of some key input parameters, including building size, material and structure, schedule, weather profile window-wall ratio, and shading length. The proposed revised model can be easily implemented in a graphics software named SketchUp. In this research, we illustrate how the proposed simplified simulation process can be easily used to explore the effects of building energy consumption as functions of different eaves size. Results show that eave size is a significant factor for moderate-size buildings and that the most cost-effective eave length is 0.25 meter.

摘要 i
英文摘要 ii
目錄 iii
表目錄 v
圖目錄 vi
第1章 緒論 1
1.1研究背景 . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2研究動機與目的 . . . . . . . . . . . . . . . . . . . . . 2
1.3使用之模擬軟體介紹 . . . . . . . . . . . . . . . . . . . . 3
1.3.1 Energyplus . . . . . . . . . . . . . . . . . . . . . 3
1.3.2 SketchUp . . . . . . . . . . . . . . . . . . . . . . 7
1.4 論文架構 . . . . . . . . . . . . . . . . . . . . . . . 8
第2章 文獻探討 9
2.1 以模擬工具為基礎的能源消耗模擬 . . . . . . . . . . . . . . 9
2.2 以演算法為基礎的能源消耗預測 . . . . . . . . . . . . . . . 10
2.3 重要參數整理 . . . . . . . . . . . . . . . . . . . . . 12
第3章 建構 「估計建築物耗能」 之流程 14
3.1 「估計建築物耗能」 概念與模擬流程 . . . . . . . . . . . . . 15
3.1.1 名詞解釋 . . . . . . . . . . . . . . . . . . . . . . 15
3.1.2 模擬之概念與流程 . . . . . . . . . . . . . . . . . . . 16
3.2 修改 「建築物耗能之重要參數」 流程 . . . . . . . . . . . . 17
3.2.1 修改 「建築物面積」 之模擬流程 . . . . . . . . . . . . . 17
3.2.2 修改 「材料結構」 之模擬流程 . . . . . . . . . . . . . 20
3.2.3 修改 「排程」 之模擬流程 . . . . . . . . . . . . . . . 22
3.2.4 修改 「天氣文件」 之模擬流程 . . . . . . . . . . . . . 25
3.2.5 修改 「窗牆比」 之模擬流程 . . . . . . . . . . . . . . 27
3.2.6 新增 「屋簷」 之模擬流程 . . . . . . . . . . . . . . . 30
3.3 小結 . . . . . . . . . . . . . . . . . . . . . . . . 32
第4章 應用-較佳屋簷長度探討 33
4.1 案例介紹 . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 不同窗牆比的兩建築物模型 . . . . . . . . . . . . . . . . 34
4.3 相同窗牆比的兩建築物模型 . . . . . . . . . . . . . . . . 36
第5章 結論與未來展望 40
5.1 結論 . . . . . . . . . . . . . . . . . . . . . . . . 40
5.2 未來展望 . . . . . . . . . . . . . . . . . . . . . . . 41
參考文獻 43
A 建築物能源消耗量的模擬結果 46

[1] Bourgeois, D., Reinhart, C., & Macdonald, I. (2006). Adding advanced behavioural models in whole building energy simulation: a study on the total energy impact of manual and automated lighting control. Energy and Buildings, 38(7), 814-823.
[2] Boyano, A., Hernandez, P., & Wolf, O. (2013). Energy demands and potential savings in European office buildings: Case studies based on EnergyPlus simulations. Energy and Buildings, 65, 19-28.
[3] Crawley, D. B., Hand, J. W., Kummert, M., & Griffith, B. T. (2008). Contrasting the capabilities of building energy performance simulation programs. Building and environment, 43(4), 661-673.
[4] Dong, B., Cao, C., & Lee, S. E. (2005). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37(5), 545-553.
[5] Ekici, B. B., & Aksoy, U. T. (2009). Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software, 40(5), 356-362.
[6] Eskin, N., & Turkmen, H. (2008). Analysis of annual heating and cooling energy requirements for office buildings in different climates in Turkey. Energy and Buildings, 40(5), 763-773.
[7] HE, C. Q. (2014). Analysis of small and medium-sized public building energy consumption in hot summer and cold winter area. Journal of Chemical and Pharmaceutical Research, 6(4), 498-503.
[8] Lam, J. C. (2000). Energy analysis of commercial buildings in subtropical climates. Building and Environment, 35(1), 19-26.
[9] Lam, J. C., Wan, K. K., Wong, S. L., & Lam, T. N. (2010). Principal component analysis and long-term building energy simulation correlation. Energy Conversion and Management, 51(1), 135-139.
[10] Li, K., Su, H., & Chu, J. (2011). Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study. Energy and Buildings, 43(10), 2893-2899.
[11] Neto, A. H., & Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and Buildings, 40(12), 2169-2176.
[12] Newsham, G. R., & Birt, B. J. (2010, November). Building-level occupancy data to improve ARIMA-based electricity use forecasts. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (pp. 13-18). ACM. 44
[13] Perez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and buildings, 40(3), 394-398.
[14] Tso, G. K., & Yau, K. K. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761-1768.
[15] Yu, J., Yang, C., & Tian, L. (2008). Low-energy envelope design of residential building in hot summer and cold winter zone in China. Energy and Buildings, 40(8), 1536-1546.