為提供人體所需，建築內部需能適時進行空氣調節。典型的空調系統雖能因應溫濕度的變異，以調節室內空氣；然其易損耗相當的成本與能源，更何況此種封閉式架構，實難以適時的排除危害氣體。相對的，開放式通風架構雖消耗較少能源，可提供相當的室內空氣條件，然其所導致的噪音與粉塵，亦將降低整體室內品質。因此，如何同時考量空氣安全、舒適條件與操作成本等多方面需求，實為目前建築控制系統的重要議題。
本論文提出一智慧型控制系統，以提供建築具有調節呼吸的能力。在架構規劃上，本系統可根據需求，彈性設定不同感測器於不同位置，包含保護人體安全的一氧化碳和烷系感測器；保持環境舒適度的溫濕度、二氧化碳與空氣品質感測器；與監視能源消耗的電流計等。經設定後，系統將可對應其指標如安全、舒適度與成本等進行監控。在空氣調節分析上，可根據自適應性網路模糊推論系統(Adaptive Network-based Fuzzy Inference System, ANFIS)，以自動建立建築內氣體動力模型。最後在空氣調節控制上，則可透過灰預測方法，基於建築氣體模型的推估，以事先或動態調節設備，以達到舒適度與成本的最舒適控制。
在實驗上，透過兩種不同建築結構，分別為小木屋與獨棟木屋，前者具自動窗與多重不同風扇，後者則配以動態通風井與多重抽風扇，兩者均透過所提系統與感測器，以整合其自然與強制對流機制。在低碳屋中利用煙囪效應使室內降溫的實驗下，可以得知未使用智慧型控制方法時室內溫度會低於室外溫度一度。若是再藉由抽風扇加強對流的情況下，室內可較室外溫度低約三度，因此利用建築設計架構與自然對流結合機械通風可以增加房屋室內的對流性，以達到室內降溫與空氣循環的效果。透過多階段累積危害氣體濃度的狀態機，本系統除可提供特定氣體自動警示與排除，具安全保護能力外；系統更結合ANFIS與灰預測控制法，在保持室內外溫差達三度以上，提供具維持舒適度的能力，並進一步較條件控制法減少16%的能源消耗。

A building should timely regular air conditions for human body needs. Although an air conditioner is popularly used to regular variations of temperature and humidity, it costs high investment and energy. Moreover, this kind of close structure fails to timely extract dangerous gas to outside. An open ventilation structure consumes less power while providing well air conditions; however, it reduces indoor quality by causing high noise and more particles. Therefore, an important issue of development of a building control system is how to reach requirements, i.e. safety, comfort, and cost, simultaneously.
This work proposes an intelligent control system enabling a building with self-respiration capability. In structure configuration, this system allows users to flexibly configure various locations and sensors such as safety protection, e.g., CO and alkane sensors; comfort keeping, e.g., temperature, humidity, CO2, and air-quality; energy monitoring by current meter. After configuration of sensing sources, this system then can monitor corresponding indices including safety, comfort, and cost. In gas analysis, an adaptive network-based fuzzy inference system (ANFIS) is adopted to model gas dynamics of a building. Finally, by using a grey prediction with the gas dynamics estimation, the system can regulate devices in advance for reaching both objectives of comfort and cost.
In experiments, two different buildings without any air conditioner were performed, in which a small cabin is with auto windows and various fans; a two-floor townhouse is with a ventilate well and four fans. Both buildings are integrated by the proposed control system to provide ventilation for self-respiration. Experimental results show that the proposed system can detect and extract dangerous gas for protecting safety based on a multi-level a state machine, which transits operating states by accumulating densities of dangerous gas. Furthermore, while comparing to a condition-based method, this system keeps lower temperature than outdoors 3℃ and decreases lower energy consumption 16 % by integrating ANFIS estimation and grey prediction.

摘要 I
致謝 III
目錄 IV
表目錄 V
圖目錄 VI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 系統指標 4
1.4 論文架構 8
第二章 智慧型調節控制方法 10
2.1 智慧型控制系統技術簡介 10
2.2 智慧型控制系統方法 16
2.2.1 智慧型控制系統架構 16
2.2.2 智慧型演算法架構 18
第三章 智慧型系統分析與設計 30
3.1 智慧型調節控制系統之系統分析 30
3.2 智慧型調節控制系統之系統設計 38
第四章 系統實驗結果與分析 47
4.1 實驗系統描述 47
4.1.1 實驗環境介紹 47
4.1.2 實驗硬體與軟體架構 51
4.2 實驗結果與分析 55
4.2.1 條件控制(Condition-based Control) 55
4.2.2 自適應性網路模糊推論系統(ANFIS-based Control) 63
4.2.3 灰預測(Grey Prediction) 68
4.2.4 灰色預測與ANFIS系統整合 76
4.3 實驗結果討論 79
第五章 結論與未來研究 82
5.1 結論 82
5.2 未來研究 83
參考文獻 84

[1] D. Snoonian, ‘‘Smart buildings,” IEEE Specturm, Vol. 40,
pp.18-23, 2003.
[2] 財團法人台灣建築中心(民國100年06月29日)。智慧型建築標章。【網站圖
文資料】。取自http://iblogo.tabc.org.tw/ibsm/p-1.aspx
[3] W. Kastner, G. Neugschwandtner, S. Soucek, and H.M.
Newman, ‘‘Communication Systems for Building Automation
and Control,” IEEE Proceedings, Vol. 93, no.6, 2005.
[4] M. Chan, E. Campo, D. Esteve, and J. Fourniols, ‘‘Smart
homes-Current features and future perspectives,” Science
Direct Maturitas, Vol. 64, pp.90-97, 2009.
[5] D. Ding, R. A. Cooper, F.P. Paul, and F.P. Lavinia,
‘‘Sensor Technology for smart homes,” ScienceDirect
Maturitas, Vol. 69, pp.131-136, 2011.
[6] 張育陞，”智慧型家庭網路的建置”，明志科技大學工程技術研究所碩士論文，
2007。
[7] 龍國強，”應用電力線通訊於家庭自動化網路之設計與實作”，國立成功大學工
程科學研究所碩士論文，2006。
[8] 黃雅綾，”居家之智慧型安全系統介面研究”，國立台北科技大學創新設計研究
所碩士論文，2002。
[9] 楊語芸(民國99年12月)。智慧綠建築開啟住宅新時代。Taiwan Economic
Forum，Vol.8, no.12，p.29-32。取自
http://www.cepd.gov.tw/book/m1.aspx?sNo=0015238.
[10] N. Papadopoulos, A. Meliones, D. Economou, I. Karras,
and I. Liverezas, ‘‘A
Connected Home Platform and Development Framework for
Smart Home Control Applications,” IEEE International
Conference on Industrial Informatics, pp.402-409,
2009.
[11] H. Joumaa, S. Ploix, S. Abras, and G. De Oliveire, ‘‘A
MAS integration into Home Automation system, for the
resolution of power management problem in smart
homes,’’ ScienceDirect EnergyProcedia, Vol. 6, pp.786-
794, 2011.
[12] G. Wood, M. Newborough, ‘‘Energy-use information
transfer for intelligent homes:
Enabling energy conservation with central and local
displays,’’ ScienceDirect Energy
and Buildings, Vol. 39, Issue 4, pp.495-503, 2007.
[13] S.H. Baeg, J.H. Park, J. Koh, K.W. Park, M.H.
Baeg,‘‘Building a Smart Home Environment for Service
Robots Based on RFID and Sensor Networks, ‘‘ IEEE
International Conference on Control, Automation and
Systems, pp.1078-1082, 2007.
[14] A.I. Dounisa, P. Tiropanisb, A. Argiriouc, and A.
Diamantisa,‘‘Intelligent control system for
reconciliation of the energy savings with comfort in
buildings using soft computing techniques,’’ Science
Direct Energy and Buildings, Vol. 43, Issue 1,
pp.66-74, 2011.
[15] C. Leong, A.R. Ramli, and T. Perumal, ‘‘A Rule-Based
Framework for Heterogeneous Subsystems Management in
Smart Home Environment,’’ IEEE Transactions on Consumer
Electronics, Vol. 55, pp.1208-1213, 2009.
[16] Y. Zhang, X. Yang, ‘‘Design of Phone Remote Measurement
and Control System in Intelligent Building,’’ IEEE Ninth
International Conference on Electronic
Measurement & Instruments, pp.92-95, 2009.
[17] 朱振豪，”住宅部門節電措施及CO2減量之探討-以冷氣空調為例”，國立台北
大學自然資源與環境管理研究所碩士論文，2006。
[18] National Oceanic & Atmospheric Administration, NOAA.
美國國家海洋與大氣管理。 Retrieved from
http://www.noaa.gov/media.html
[19] ASHERAE Standard55-2004, Thermal environmental
conditions for human occupancy, Table 5.2.1.2.
[20] 陳勇華，”智慧型感測器整合式無線傳輸系統研製”，逢甲大學通訊工程研究
所碩士論文，2004。
[21] 陳彥翔，”無線感測器網路中的容錯機制”，國立成功大學資訊工程研究所碩
士論文，2004。
[22] J. Herbert, J. O’Donoghue, and X. Chan, ‘‘A Context-
Sensitive Rule-based Architecture for a Smart Building
Environment,” Second International Conference on Future
Generation Communication and Networking, Vol. 2, pp.437-
440, 2008.
[23] H. Chen, P. Chou, S. Duri, H. Lei, J. Reason, ‘‘The
Design and Implementation of a Smart Building Control
System,” IEEE Internationa l Conference on e-Business
Engineering, pp.255-262, 2009.
[24] R. Morello, C. De Capua, and A. Meduri, ‘‘Remote
Monitoring of Building Structural Integrity by a Smart
Wireless Sensor Network,” IEEE Instrumentation and
Measurement Technology Conference , pp.1150-1154, 2010.
[25] ZigBee Alliance。Zigbee技術聯盟。Retrieved from
http://www.zigbee.org/
[26] Y. Zhou, X. Yang, X. Guo, M. Zhou, and L. Wang,“A
Design of Greenhouse Monitoring & Control System Based
on ZigBee Wireless Sensor Network,” IEEE Wireless
Communication, Networking and Mobile Computing, pp.2563-
2567, 2007.
[27] P. Ferrari, A. Flammini, D. Mariolo, E. Sisinni and A.
Taroni, “A Bluetooth-based sensor network with Web
interface,” IEEE Transactions on Instrumentation and
Measurement, Vol. 54, Issue 6, pp.2359-2363, 2005.
[28] HomePlug PowerLine Alliance, Retrieved from
http://www.homeplug.org/en/index.asp, December 2005.
[29] J.-S. R. Jang, “ANFIS: Adaptive-Network-based Fuzzy
Inference Systems,” IEEE Trans. on Systems, Man, and
Cybernetics, Vol. 23, pp.665-685, 1993.
[30] H. Ying, Y. Ding, S. Li, and S. ,“Comparison of
Necessary Conditions for Typical Takagi-Sugeno and
Mamdani Fuzzy Systems as Universal Approximation,” IEEE
Trans. on Systems, Man, and Cybernetics-PART A: systems
and humans, pp.508-514, 1999.
[31] S. Soyguder and H. Alli, “An expert system for the
humidity and temperature control in HVAC systems using
ANFIS and optimization with Fuzzy Modeling Approach,”
ScienceDirect Energy and Buildings, pp.814-822, 2009.
[32] Q. Zou, J.W. Ji, Q.G. Li, and R. Huang, “Application of
ANFIS for Modeling and Simulation of the Greenhouse
Environment,” IEEE International Conference on
Industrial Mechatronics and Automation, pp.29-32, 2010.
[33] 楊國珍，”類神經模糊系統應用於蒸發量推估之研究”，國立成功大學水利及
海洋工程研究所碩士論文，2004。
[34] 蔡獲光，”應用ANFIS模式於東部海域之季節風波浪推算”，國立交通大學土
木工程研究所碩士論文，2006。
[35] 蘇漢昌，”應用適應性模糊推論系統改善類神經網路預測工業廢水場出流水水
質之研究”，朝陽科技大學環境工程與管理研究所碩士論文，2008。
[36] J.L. Deng, “Control problems of grey system,” System
and Control Letters, Vol. 5, pp.288-294, 1982.
[37] 鄧聚龍、郭洪，”灰色預測原理與應用”，全華科技圖書股份有限公司，
1996。
[38] 徐福燦，”灰色理論應用於颱風降雨量推估之研究-以嘉義地區為例”， 國立
成功大學水利及海洋工程研究所碩士論文，2004。
[39] 藍宏智，”結合灰色理論與模糊控制之空調系統舒適及節能之研究”，國立彰
化師範大學工業教育研究所碩士論文，2000。
[40] 陳憲宗，”灰色預測在逕流預報之應用”，國立成功大學水利及海洋工程研究
所碩士論文，1997。
[41] Y. Zhao, Z.F. Song, “The Intelligent Controller of
Filling Liquid Nitrogen Based on the Grey Prediction
Theory,” IEEE International Conference on Computer
Design And Applications, Vol. 3, pp.274-277, 2010.
[42] Q.Y. Shou, ‘‘A New Central Air-Conditioning System
Control Strategy based on Prediction,” IEEE
International Conference on Computer Science and
Information Technology, Vol. 6, pp.479-482, 2010.