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研究生:江成龍
研究生(外文):CHANON JEAMBURASETH
論文名稱:臺灣臺北大型建築能耗模型校正
論文名稱(外文):Calibrated Building Energy Simulation for a Large Building in Taipei, Taiwan
指導教授:李文興李文興引用關係
指導教授(外文):LEE, WEN-SHING
口試委員:李文興陳希立陳韋任柯明村
口試委員(外文):LEE, WEN-SHINGCHEN, SIH-LICHEN, WEI-JENKE, MING-TSUN
口試日期:2020-06-17
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:能源冷凍空調與車輛工程外國學生專班
學門:工程學門
學類:其他工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:42
外文關鍵詞:Energy-PlusCalibrationLarge-buildingNMBE and CV(RMSE)
相關次數:
  • 被引用被引用:0
  • 點閱點閱:129
  • 評分評分:
  • 下載下載:22
  • 收藏至我的研究室書目清單書目收藏:0
The easiest to manage the entire building to save energy consumption is to use the Building energy simulation to find the best strategies to manage the entire building in one hand. The more energy consumption reduces, the more money saving in the account. The Air conditioning systems occupy 60% of the total energy consumption of commercial buildings. The more understanding about air conditioning system and cooling load, the more optimization can make all of them are money.
It would be better if every house in the town or every factory in Taiwan are possible to have a possibility to predict the total energy consumption for their place, then they can manage how to control their energy consumption for saving their income to spend for another interesting thing than electricity bill.
The accuracy of PSO method combine with Energy-Plus possible to predict the result with the NMBE value 0.79% and CV(RMSE) value 3.08%. That means this combine method possible to predict the cooling load for large building.
ABSTRACT i
Acknowledgements iii
Table of Contents iv
List of Tables v
List of Figures vi
Chapter 1 Introduction 1
Chapter 2 Introduction 5
2.1 Data Collection 5
2.2 Setup the building, run and collect result 6
2.2.1 OpenStudio Program 6
2.2.2 Energy-Plus 13
Chapter 3 Energy Model 24
3.1 Building characteristics 24
3.2 Building Model 24
Chapter 4 Result and Discussion 29
Chapter 5 Conclusion 39
Chapter 6 Reference 41
[1] Bureau of Energy,Ministry of Economic Affairs「經濟部能源局組織條例」
[2] Yiqun Pan, Zhizhong Huang, Gang Wu,2006. Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai. Energy and Buildings 39 (2007) 651–657.
[3] Mohammad Royapoor, Tony Roskilly.2015 Building model calibration using energy and environmental data. Energy and Buildings 94 (2015) 109–120.
[4] Valentina Monetti, Elisabeth Davin, Enrico Fabrizio, Philippe André, Marco Filippi1.2015. Calibration of building energy simulation models based on optimization: a case study. Energy Procedia 78 (2015) 2971–2976.
[5] Zheng O’Neill1, Bryan Eisenhower.2013. Leveraging the analysis of parametric uncertainty for building energy model calibration. O’Neill and Eisenhower / Building Simulation / Vol. 6, No. 4(2015) 365–377.
[6] Zheng Yang, Burcin Becerik-Gerber.2015. A model calibration framework for simultaneous multi-level building energy simulation. Applied Energy 149 (2015) 415–431.
[7] LAM, Khee Poh, ZHAO, Jie, YDSTIE, Erik B., WIRICK, Jason, QI, Meiwei and PARK, Ji Hyun (2014). An EnergyPlus whole building energy model calibration method for office buildings using occupant behavior data mining and empirical data.ASHRAE Journal, 160-167.
[8] Germán Ramos Ruiz Carlos Fernández Bandera.2017. Validation of Calibrated Energy Models: Common Errors.2017. Energies 2017.
[9] Pei-Ling Wen, Ching-Wei Kuo, Tzu-Yar Liu,Ming-Lung Hung.Load pattern and the deployment of renewables in Taiwan within the TIMES framework.
[10] Zheng O’Neill, Bryan Eisenhower, Shui Yuan, Trevor Bailey, Satish Narayanan, Vladimir Fonoberov. 2011.Modeling and Calibration of Energy Models for a DoD Building. ASHREA Transactions (2011) 358-365
[11] Fernando Simon Westphal,Roberto Lamberts.2005. BUILDING SIMULATION CALIBRATION USING SENSITIVITY ANALYSIS.Building simulation (2005) 1331-1338.
[12] Arga Adyatama.2019. Particle Swarm Optimization. https://rpubs.com/Argaadya/intro-PSO
[13] EnergyPlus. https://energyplus.net/
[14] OpenStudio. https://www.openstudio.net/
[15] Claus Bendtsen.2015. Particle Swarm Optimization.CRAN
[16] Clerc, M. et al. (2010) http://www.particleswarm.info/standard_pso_2007.c except when type is “SPSO2011” in which case the differences described in Clerc, M. (2011) http://www.particleswarm.info/SPSO_descriptions.pdf apply. Notice that the SPSO 2011 implementation does not include any of the bells and whistles from the implementation by M. Clerc et al. and effectively only differes from the SPSO 2007 implementation in the default swarm size, how velocities are initiated and the update of velocities/positions which in the SPSO 2011 implementation are invariant to rotation.

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