臺灣博碩士論文加值系統

目前全球因為高度開發使得電量有不夠用的危機，又因為發電廠會排放二氧化碳造成全球暖化及核電廠會汙染環境和輻射外洩與爆炸的危險因素，所以持續增建發電廠不是一個好的辦法，因著科技的日益進步我們可以用更好的方案來解決電量不夠的問題，在許多用電當中，家庭用電算是僅次於工業用電的，工業用電關乎於國家經濟方展，要限制用電是有難度的，所以家庭用電就是關鍵所在，必須發展一套智慧家庭能源管理系統來有效率的管理用電量，此系統已經有好幾篇相關的研究，其中一種方式就是藉由時間電價來進行排班，可將一些不會直接影響生活作息的家電做排班，基本的方法就是在尖峰電價高的時候盡量不安排家電運作，把大部分家電的運作時間都集中在離峰電價低的時段，這樣就可以使整體的用電量有明顯的減少，但是此方法在台灣可能無法真正的落實，因為台灣雖然也有這種時間電價的計算方式但是使用的人卻是少之又少，大部分的人還是使用傳統的計價方式，也就是一天當中不分時段依照電價來計價，但是有兩個外加條件就是電價會依使用的度數相應的調高，且電價也有分為夏季電價和非夏季電價的區別，這是在計算電費時需要注意的，所以我依照台灣國人電費計價的習慣來研究出類似上述所提排班方式，發展出一個排班演算法，來達到客戶預期省電效果。

One reason that causes world crisis upon our living environments is the effects of global warming caused by substantive carbon dioxide emissions produced by electrical diffuses of power plants industries and family energy uses of electricity consumers. Consequently, for the purpose of reducing this hazardous aftermath, our aim in this paper was not only to propose an intelligent scheduling system for reducing energy consumption costs for smart home users, but ,eventually, also could provide certain contributions for reducing these global warming effects more. We therefore proposed a framework using genetic algorithms for optimized home appliances energy uses scheduling. Our extensive experiments showed that our proposed framework could not just provide the optimized scheduling for home appliances uses, with the pre-configured monthly energy expenditure limits, but also could still retain the living comfort of the residents.

摘要....................................................i
Abstract..............................................iii
誌謝.................................................. iv
目錄....................................................v
表目錄...............................................viii
圖目錄.................................................ix
第一章 緒論..............................................1
1.1 研究背景與動機.......................................1
1.2 研究目的.............................................3
第二章 文獻探討..........................................4
2.1 電價分類.............................................4
2.2 家庭能源管理系統......................................7
2.3基因演算法...........................................13
2.3.1基因演算法流程.....................................13
2.3.2染色體............................................14
2.3.3適應函數...........................................14
2.3.4基因演算法的運算子..................................14
2.3.5基因演算法的流程圖..................................16
2.4論文架構.............................................17
第三章 研究方法.........................................18
3.1家電設定.............................................18
3.1.1即時電器...........................................18
3.1.2非即時電器.........................................20
3.2電費計價方式.........................................21
3.3問題制定.............................................24
3.4排班演算法...........................................25
3.4.1適應函數...........................................25
3.4.2編碼..............................................26
3.4.3選擇.複製..........................................26
3.4.4交換..............................................27
3.4.5突變..............................................28
3.5演算法流程圖.........................................29
3.6虛擬程式碼...........................................29
第四章 實驗結果與討論....................................32
4.1數值模擬實驗.........................................32
4.2重新定義問題.........................................47
4.3硬體................................................56
4.4軟體................................................63
第五章 結論.............................................68
參考文獻................................................71
Extended Abstract......................................73
簡歷...................................................76

[1]S. Aman, Y. Simmhan, and V. K. Prasanna, "Energy management systems: state of the art and emerging trends," IEEE Communications Magazine, vol. 51, pp. 114-119, 2013.
[2]黃怡碩, "我國智慧電網發展現況," 2013.
[3]S. Kin Cheong, J. Weimer, H. Sandberg, and K. H. Johansson, "Scheduling smart home appliances using mixed integer linear programming," in Proceedings of 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 2011, pp. 5144-5149.
[4]A. J. Conejo, J. M. Morales, and L. Baringo, "Real-Time Demand Response Model," IEEE Transactions on Smart Grid, vol. 1, pp. 236-242, 2010.
[5]N. Kunwar, K. Yash, and R. Kumar, "Area-Load Based Pricing in DSM Through ANN and Heuristic Scheduling," IEEE Transactions on Smart Grid, vol. 4, pp. 1275-1281, 2013.
[6]B. Jinsung, H. Insung, and P. Sehyun, "Intelligent cloud home energy management system using household appliance priority based scheduling based on prediction of renewable energy capability," IEEE Transactions on Consumer Electronics, vol. 58, pp. 1194-1201, 2012.
[7]J. Hyung-Chul, K. Sangwon, and J. Sung-Kwan, "Smart heating and air conditioning scheduling method incorporating customer convenience for home energy management system," IEEE Transactions on Consumer Electronics, vol. 59, pp. 316-322, 2013.
[8]S. Young-Sung, T. Pulkkinen, M. Kyeong Deok, and K. Chaekyu, "Home energy management system based on power line communication," IEEE Transactions on Consumer Electronics, vol. 56, pp. 1380-1386, 2010.
[9]S. Salinas, L. Ming, and L. Pan, "Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids," IEEE Transactions on Smart Grid, vol. 4, pp. 341-348, 2013.
[10]H. Jinsoo, C. Chang-Sic, and L. Ilwoo, "More efficient home energy management system based on ZigBee communication and infrared remote controls," IEEE Transactions on Consumer Electronics, vol. 57, pp. 85-89, 2011.
[11]X. Gang, C. Chen, S. Kishore, and A. Yener, "Smart (in-home) power scheduling for demand response on the smart grid," in Proceedings of 2011 IEEE PES Innovative Smart Grid Technologies (ISGT), 2011, pp. 1-7.
[12]C. H. Lu, C. L. Wu, M. Y. Weng, W. C. Chen, and L. C. Fu, "Context-Aware Energy Saving System with Multiple Comfort-Constrained Optimization in M2M-Based Home Environment," IEEE Transactions on Automation Science and Engineering, vol. PP, pp. 1-15, 2015.
[13]P. O. Fanger, "Thermal environment — Human requirements," Environmentalist, vol. 6, pp. 275-278.
[14]N. Hassan, M. Pasha, C. Yuen, S. Huang, and X. Wang, "Impact of Scheduling Flexibility on Demand Profile Flatness and User Inconvenience in Residential Smart Grid System," Energies, vol. 6, p. 6608, 2013.
[15]M. Yagiura and T. Ibaraki, "The use of dynamic programming in genetic algorithms for permutation problems," European Journal of Operational Research, vol. 92, pp. 387-401, 7/19/ 1996.
[16]台灣電力公司. 電價表. Available: http://www.taipower.com.tw/content/q_service/q_service02.aspx