運用MMR系統建構智慧綠建築節能管理模式

To Construct a Managerial Model of Smart Green Building for Energy Saving through MMR System

10.3966/101632122020090113002

廖俊茂(Jun-Mao Liao)；張陸滿(Luh-Maan Chang)；張明瑞(Ming-Jui Chang)

耗電密度 ； 綠建築 ； 機器學習 ； 智慧建築 ； 支援向量機 ； Energy Use Intensity ； Green Building ； Machine Learning ； Smart Building ； Support Vector Machine

建築學報

113期（2020 / 09 / 30）

25 - 45

繁體中文

面對氣候變遷能源短缺，政府持續推動智慧綠建築以為因應。因此探討最高等級智慧綠建築標章的建築，於運轉期間如何發揮節能最大績效至為重要。本研究以中台灣產業創新園區為例，於運轉期間，運用 Measure量測、Manage管理、Reduce節電系統，整合微智慧電網系統及建築資訊模型（Building Information Modeling）之空間管理資訊，彙整出各類空間及設施系統之詳細能耗資料，並以時間序列及影響耗電因子進行統計分析，運用線性回歸及支援向量機技術，訂定各空間及設施系統的耗能基線，作為運轉調控，建構一套節能管理模式，以發揮智慧綠建築較佳節能效益。本研究除揭露研發大樓各類空間及設施詳細耗電資訊外，其節能管理的建模架構，將可供建築師、業主、物業管理單位進入智慧綠建築運轉階段時之參考使用。

To cope with energy shortage problem, Taiwan Government continues to promote smart green buildings. Therefore, it is important to study how to maximize the operational performance of energy-saving for those smart green buildings, particularly for those receiving the highest-level smart green building badge certificate. The purpose of this paper is to present a managerial energy saving model for smart green buildings. This study used the case of Central Taiwan Industrial Innovation Campus to exemplify the managerial model. During operation, the Measure, Manage, Reduce system was used to integrate the micro-smart grid system and building information model with space management information. Thus, detailed energy consumption data for various types of space and facility system were aggregated. Meanwhile, statistical analyses were performed in terms of time sequence and influencing power consumption factors. Moreover, linear regression and support vector machine technology were used to specify the energy consumption baseline for each space and facility system. Then, a set of energy-saving management models for operation control was constructed to enhance the effective energy saving for smart green buildings. In addition to revealing power consumption information in detail for various types of space and facility of the R & D building, the modeling framework can be used by architects, property owners, and property managers as a useable reference for energy-saving management.

1. 林憲德, H. T.,賴柏亨, P. H.(2010)。辦公大樓空調形式與耗能特性之研究。建築學報，74，27-44。
   連結：
2. 陳怡蓉, Y. R.,林憲德, H. T(2017)。以建築外殼耗能指標 ENVLOAD 預測空調最大空調負荷之研究－以辦公類與百貨類建築為例。建築學報，99，21-35。
   連結：
3. 蘇瑛敏, Y. M.(2016)。BIM 導入學校類建築設施管理研究-以小學綠建築能源設備為例。建築學報，95，19-34。
   連結：
4. 產業節能減碳資訊網（2010）。能源管理系統資訊-能源績效指標。引用於 2020 年 03 月 23 日，取自https://ghg.tgpf.org.tw/Counseling/counseling_sm8_6。Industrial Energy Saving and Carbon Reduction Information Web (2010). Energy Management System Information-Energy Performance Indicators. Quoted on March 23, 2020, taken from https://ghg.tgpf.org.tw/Counseling/counseling_sm8_6.
5. Ahmed, M. S.,Mohamed, A.,Shareef, H.,Homod, R. Z.,Ali, J. A.(2016).Artificial neural network based controller for home energy management considering demand response events.2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES)
6. Bansal, A.,Rompikuntla, S. K.,Gopinadhan, J.,Kaur, A.,Kazi, Z. A.(2015).Energy consumption forecasting for smart meters.BAI Conference,6(4),1882-2001.
7. Bhattacharya, S.(2017).Berkeley,Electrical Engineering and Computer Sciences University of California.
8. Brennenstuhl, M.,Zeh, R.,Otto, R.,Pesch, R.,Stockinger, V.,Pietruschka, D.(2019).Report on a plus-energy district with low-temperature DHC network, novel agrothermal heat source, and applied demand response.Appl. Sci.,9,5059.
9. Chang, M. J.,Chang, H. K.,Chen, Y. C.,Lin, G. F.,Chen, P. A.,Lai, J. S.,Tan, Y. C.(2018).A support vector machine forecasting model for typhoon flood inundation mapping and early flood warning systems.Water,10(12),1734.
10. Cho, S.,Lee, J.,Baek, J.,Kim, G. S.,Leigh, S. B.(2019).Investigating primary factors affecting electricity consumption in non-residential buildings using a data-driven approach.Energies,12(21),4046.
11. Cristianini, N.,Shaw–Taylor, J.(2000).An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.NY, USA:Cambridge University Press.
12. De Silva, M. N. K.,Sandanayake, Y. G.(2012).Building energy consumption factors: a literature review and future research agenda.World Construction Conference 2012 –Global Challenges in Construction Industry
13. Deb, C.,Zhang, F.,Yang, J.,Lee, S. E.,Shah, K. W.(2017).A review on time series forecasting techniques for building energy consumption.Renewable and Sustainable Energy Reviews,74,902-924.
14. Han, J. S.,Jeong, Y. K.,Lee, I. W.(2013).Analysis of electric energy consumption patterns: A case study of a real life office building.Applied Mechanics and Materials,330,158-162.
15. IEA (2019). Building material interactions and perceived air quality. World Energy Outlook 2019. Retrieved March 23, 2020 from http://www.worldenergyoutlook.org/.
16. Kamilaris, A.,Kalluri, B.,Kondepudi, S.,Wai, T. K.(2014).A literature survey on measuring energy usage for miscellaneous electric loads in offices and commercial buildings.Renewable and Sustainable Energy Reviews,34,536-550.
17. Lin, G. F.,Chang, M. J.,Huang, Y. C.,Ho, J. Y.(2017).Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression.Engineering Geology,224,62-74.
18. Lin, G. F.,Chang, M. J.,Wu, J. T.(2017).A hybrid statistical downscaling method based on the classification of rainfall patterns.Water Resources Management,31(1),377-401.
19. Petroleum, B. (2020). Statistical Review of World Energy. Retrieved from https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2019-full-report.pdf. page 2.
20. Reeves, T.,Olbina, S.,Issa, R.(2012).Validation of building energy modeling tools: EcotectTM , Green Building Studio TM and IES TM.Proceedings of the 2012 Winter Simulation Conference (WSC),Berlin:
21. Ryan, E. M.,Sanquist, T. F.(2012).Validation of building energy modeling tools under idealized and realistic conditions.Energy Build,47,375-382.
22. Safa, M.,Allen, J.,Safa, M.(2014).Predicting energy usage using historical data and linear models.ISARC
23. Vapnik, N.V.(2000).The Nature of Statistical Learning Theory.NY, USA:Springer.
24. Wang, Z.,Srinivasan, R.,Wang, Y.(2016).Homogeneous Ensemble Model for Building Energy Prediction: A Case Study Using Ensemble Regression Tree.Proceedings of the 2016 ACEEE Summer Study on Energy Efficiency in Buildings,Pacific Grove, CA, USA:
25. Wang, Z.,Wang, Y.,Zeng, R.,Srinivasan, R. S.(2018).Ahrentzen, S. Random Forest based hourly building energy prediction.Energy Build,171,11-25.
26. Williams, K. T.,Gomez, J. D.(2016).Predicting future monthly residential energy consumption using building characteristics and climate data: A statistical learning approach.Energy Build,128,1-11.
27. Woo, Y. E.,Cho, G. H.(2018).Impact of the surrounding built environment on energy consumption in mixed-use building.Sustainability,10(3),832.
28. Yalcintas, M.,Ozturk, U. A.(2007).An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consump- tion Survey (CBECS) database.Int. J. Energy Res,31(4),412-421.
29. Yoshino, H.(2016).Project Summary ReportProject Summary Report,United Kingdom:AECOM Ltd.
30. Zhou, J.,Huo, X.,Xu, X.,Li, Y.(2019).Forecasting the carbon price using extreme-point symmetric mode decomposition and extreme learning machine optimized by the grey wolf optimizer algorithm.Energies,12,950.
31. 內政部=Ministry of the Interior。內政部。中台灣產業創新研發專區新建工程評定書:鑽石級候選綠建築證書。新北市：內政部建築研究所。Ministry of the Interior. Evaluation of New Projects in the Central Taiwan Industrial Innovation and Campus: Diamond Candidate Green Building Certificate. New Taipei City: Architecture and Building Research Institute, Ministry of the Interior.。
32. 內政部=Ministry of the Interior。內政部。中台灣產業創新研發專區評定書:鑽石級智慧建築標章。新北市：內政部建築研究所。Ministry of the Interior. Evaluation of New Projects in the Central Taiwan Industrial Innovation and Campus: Diamond Intelligent Building Mark. New Taipei City: Architecture and Building Research Institute, Ministry of the Interior.。
33. 內政部=Ministry of the Interior。內政部。中台灣產業創新研發專區新建工程評定書:鑽石級候選智慧建築證書。新北市：內政部建築研究所。Ministry of the Interior. Evaluation of New Projects in the Central Taiwan Industrial Innovation and Campus: Diamond Candidate Intelligent Building Certificate. New Taipei City: Architecture and Building Research Institute, Ministry of the Interior.。
34. 內政部=Ministry of the Interior。內政部。中台灣產業創新研發專區新建工程評定書:鑽石級綠建築標章。新北市：內政部建築研究所。Ministry of the Interior. Evaluation of New Projects in the Central Taiwan Industrial Innovation and Campus: Diamond Green Building Mark. New Taipei City: Architecture and Building Research Institute, Ministry of the Interior.。
35. 王婉芝, W. Z.,柯文立, W. L.,李明賢, M. X.,陳文洲, W. Z.(2016)。內政部建築研究所委託研究報告內政部建築研究所委託研究報告,新北市=New Taipei City:內政部建築研究所=Architecture and Building Research Institute, Ministry of the Interior。
36. 周世璋, S. Z.,林憲德,Lin, H. T.(2015)。內政部建築研究所委託研究報告內政部建築研究所委託研究報告,新北市=New Taipei City:內政部建築研究所=Architecture and Building Research Institute, Ministry of the Interior。
37. 林憲德, H. T.(2014).建築碳足跡(上)：評估理論篇.台北市=Taipei City:詹氏.
38. 林憲德, H. T.(2014).建築碳足跡(下)：診斷實務篇.台北市=Taipei City:詹氏.
39. 社團法人台灣智慧建築協會=Taiwan Intelligent Building Association(2017)。社團法人台灣智慧建築協會（2017）。第二屆台灣優良智慧綠建築暨系統產品獎:經濟部中台灣創新園區申請書。台北市：社團法人台灣智慧建築協會。Taiwan Intelligent Building Association (2017). The 2nd TIBA Award: Application for Central Taiwan Innovation Park of the Ministry of Economic Affairs.。
40. 財團法人綠色生產力基金會=Taiwan Green Productivity Foundation(2018)。財團法人綠色生產力基金會（2018）。2018 年非生產性質行業能源查核年報。新北市：經濟部能源局。Taiwan Green Productivity Foundation (2018). Energy Audit Annual Report for Non- Productive Industries (2018). Bureau of Energy, Ministry of Economic Affairs.。
41. 陳韋廷, W. T.,黃世昌, S. C.(2015)。新竹市=Hsinchu City，交通大學土木工程系所=Department of Civil Engineering in NCTU。
42. 陳麒任, C. J.(2015)。內政部建築研究所委託研究報告內政部建築研究所委託研究報告,新北市=New Taipei City:內政部建築研究所=Architecture and Building Research Institute, Ministry of the Interior。
43. 鄭元良, Y. L.,黃瑞隆, R. L.,黃國倉,羅時麒,陳麒任,許閔涵,傅群,董孟傑,林國華,翁堉驣(2013)。內政部建築研究所協同研究報告。創新低碳綠建築環境科技計畫協同研究計畫第 2 案內政部建築研究所協同研究報告。創新低碳綠建築環境科技計畫協同研究計畫第 2 案,New Taipei City:Architecture and Building Research Institute, Ministry of the Interior。