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研究生:羅芃駿
研究生(外文):Peng Chun Lo
論文名稱:智慧家電排程
論文名稱(外文):Scheduling of smart appliances
指導教授:林心宇
指導教授(外文):S. Y. Lin
學位類別:碩士
校院名稱:長庚大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:104
語文別:中文
論文頁數:72
中文關鍵詞:智慧家電基因演算法機率性局部搜尋最佳模擬預算分配法滾動時域
外文關鍵詞:Smart appliancesGenetic Algorithmsprobabilistic local searchOptimal Computing Budget Allocationreceding horizon
相關次數:
  • 被引用被引用:1
  • 點閱點閱:363
  • 評分評分:
  • 下載下載:86
  • 收藏至我的研究室書目清單書目收藏:0
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目錄
指導教授推薦書…………………………………………………………………………………………………………………………………………………………………………………………………
口試委員會審定書………………………………………………………………………………………………………………………………………………………………………………………………
致謝…………………………………………………………………………………………………………………………………………………………………………………………………………………………- iii -
中文摘要…………………………………………………………………………………………………………………………………………………………………………………………………………………- iv -
英文摘要…………………………………………………………………………………………………………………………………………………………………………………………………………………- v -
目錄…………………………………………………………………………………………………………………………………………………………………………………………………………………………- vi -
圖目錄……………………………………………………………………………………………………………………………………………………………………………………………………………………- ix -
表目錄……………………………………………………………………………………………………………………………………………………………………………………………………………………- x -
第一章 緒論………………………………………………………………………………………………………………………………………………………………………………………………………- 1 -
1.1研究背景………………………………………………………………………………………………………………………………………………………………………………………………………- 1 -
1.2研究動機與目的…………………………………………………………………………………………………………………………………………………………………………………………- 2 -
1.3論文架構………………………………………………………………………………………………………………………………………………………………………………………………………- 3 -
第二章 問題描述………………………………………………………………………………………………………………………………………………………………………………………………- 4 -
2.1 定義符號………………………………………………………………………………………………………………………………………………………………………………………………………- 4 -
2.2 智慧家電排程問題的形成……………………………………………………………………………………………………………………………………………………………………- 6 -
2.3 智慧家電排程問題再形成……………………………………………………………………………………………………………………………………………………………………- 7 -
2.4 滾動時域智慧家電排程…………………………………………………………………………………………………………………………………………………………………………- 8 -
第三章 解決方法………………………………………………………………………………………………………………………………………………………………………………………………- 9 -
3.1滾動時域控制機制下的智慧家電排程隨機模擬最佳化方法的概要……………………………………………………………………………………- 9 -
3.1.1 簡介…………………………………………………………………………………………………………………………………………………………………………………………………………- 9 -
3.1.2 提出的方法…………………………………………………………………………………………………………………………………………………………………………………………- 11 -
3.2 智慧家電排程的模擬方法…………………………………………………………………………………………………………………………………………………………………- 17 -
3.3 利用基因演算法搭配約化模式獲得不錯解…………………………………………………………………………………………………………………………………- 20 -
3.3.1 簡介…………………………………………………………………………………………………………………………………………………………………………………………………………- 20 -
3.3.2 提出之方法…………………………………………………………………………………………………………………………………………………………………………………………- 20 -
3.4 利用機率局部搜尋演算法獲得機率性局部最佳解…………………………………………………………………………………………………………………- 28 -
3.4.1 簡介…………………………………………………………………………………………………………………………………………………………………………………………………………- 28 -
3.4.2 提出之方法…………………………………………………………………………………………………………………………………………………………………………………………- 28 -
3.5 利用最佳模擬預算分配法獲得機率性局部最佳解中的最佳解…………………………………………………………………………………………- 31 -
3.5.1 簡介…………………………………………………………………………………………………………………………………………………………………………………………………………- 31 -
3.5.2 OCBA方法………………………………………………………………………………………………………………………………………………………………………………………………- 31 -
第四章 模擬結果與討論…………………………………………………………………………………………………………………………………………………………………………………- 34 -
4.1 智慧家電排程模擬使用的參數…………………………………………………………………………………………………………………………………………………………- 35 -
4.1.1 排程使用的參數…………………………………………………………………………………………………………………………………………………………………………………- 35 -
4.1.2 基因演算法的參數……………………………………………………………………………………………………………………………………………………………………………- 35 -
4.1.3 計算適應值模擬次數的參數…………………………………………………………………………………………………………………………………………………………- 36 -
4.1.3 實時電價的參數…………………………………………………………………………………………………………………………………………………………………………………- 37 -
4.2 模擬CASE的設計………………………………………………………………………………………………………………………………………………………………………………………- 38 -
4.3 模擬結果與比較………………………………………………………………………………………………………………………………………………………………………………………- 38 -
4.3.1 CASE A的模擬結果與比較………………………………………………………………………………………………………………………………………………………………- 39 -
4.3.2 CASE B的模擬結果與比較………………………………………………………………………………………………………………………………………………………………- 44 -
4.3.3 CASE C的模擬結果與比較………………………………………………………………………………………………………………………………………………………………- 47 -
4.3.4 CASE D的模擬結果與比較………………………………………………………………………………………………………………………………………………………………- 50 -
4.3.5 CASE E的模擬結果與比較………………………………………………………………………………………………………………………………………………………………- 53 -
4.4比較三種方法的計算時間…………………………………………………………………………………………………………………………………………………………………………- 56 -
第五章 結論……………………………………………………………………………………………………………………………………………………………………………………………………………- 57 -
參考文獻……………………………………………………………………………………………………………………………………………………………………………………………………………………- 58 -


圖目錄
圖3- 1:滾動時域控制機制的隨機模擬最佳化演算法流程圖………………………………………………………………………………………………………- 16 -
圖3- 2:智慧家電排程模擬流程圖……………………………………………………………………………………………………………………………………………………………- 19 -
圖3- 3:基因編碼方式……………………………………………………………………………………………………………………………………………………………………………………- 21 -
圖3- 4:輪盤式選擇…………………………………………………………………………………………………………………………………………………………………………………………- 23 -
圖3- 5:突變…………………………………………………………………………………………………………………………………………………………………………………………………………- 25 -
圖3- 6:基因演算法流程圖……………………………………………………………………………………………………………………………………………………………………………- 27 -
圖3- 7:機率性局部搜尋演算法流程圖……………………………………………………………………………………………………………………………………………………- 30 -
圖3- 8:OCBA流程圖…………………………………………………………………………………………………………………………………………………………………………………………- 33 -


表目錄
表4- 1:實時電價表(單位美金)………………………………………………………………………………………………………………………………………………………………- 37 -
表4- 2:CASE A的AOA家電及相關參數…………………………………………………………………………………………………………………………………………………- 39 -
表4- 3:CASE A的MOA家電及相關參數…………………………………………………………………………………………………………………………………………………- 39 -
表4- 4:預測太陽能發電量…………………………………………………………………………………………………………………………………………………………………………- 40 -
表4- 5:CASE A的太陽能發電量誤差及相關參數……………………………………………………………………………………………………………………………- 41 -
表4- 6:方法求解能力比較表(單位美金)……………………………………………………………………………………………………………………………………………- 43 -
表4- 7:CASE B的太陽能發電量誤差及相關參數……………………………………………………………………………………………………………………………- 44 -
表4- 8:方法求解能力比較表(單位美金)……………………………………………………………………………………………………………………………………………- 46 -
表4- 9:CASE C的AOA家電及相關參數…………………………………………………………………………………………………………………………………………………- 47 -
表4- 10:CASE C的MOA家電及相關參數………………………………………………………………………………………………………………………………………………- 47 -
表4- 11:方法求解能力比較表(單位美金)…………………………………………………………………………………………………………………………………………- 49 -
表4- 12:CASE D的AOA家電及相關參數………………………………………………………………………………………………………………………………………………- 50 -
表4- 13:CASE D的MOA家電及相關參數………………………………………………………………………………………………………………………………………………- 50 -
表4- 14:方法求解能力比較表(單位美金)…………………………………………………………………………………………………………………………………………- 52 -
表4- 15:CASE E的AOA家電及相關參數………………………………………………………………………………………………………………………………………………- 53 -
表4- 16:CASE E的MOA家電及相關參數………………………………………………………………………………………………………………………………………………- 53 -
表4- 17:方法求解能力比較表(單位美金)…………………………………………………………………………………………………………………………………………- 55 -
表4- 18:方法計算時間比較表(單位秒)………………………………………………………………………………………………………………………………………………- 56 -

[1] J. Han, C.-S. Choi, W.-K. Park, I. Lee and S.-H. Kim, “Smart home energy managementsystem including renewable energy based on ZigBee and PLC,” IEEE Trans. on ConsumerElectronics, vol.60, n0. 2, pp. 198-202, May 2014.
[2] D.-M. Han and J.-H. Lim, “Smart home energy management system using IEEE 802.15.4 and ZigBee,” IEEE Trans. on Consumer Electronics, vol. 56, no. 3, pp. 1403-1410, Aug.2010.
[3] Z. Zhao, W. C. Lee, Y. Shin, K.-B. Song, An optimal power scheduling method for demand 3 response in home energy management system,” IEEE Trans. on Smart Grid, vol. 3., no. 4,pp.1391-1400, Sept. 2013.
[4] Youbike Taipei [Online], http://www.youbike.com.tw (accessed March 11, 2009).
[5] C. G. Cassandras and S. Lafortune. Introduction to discrete event systems. Boston, MA.,Kluwer, 1999.
[6] G. Fishman. Discrete-event simulation: modeling, programming and analysis. Springer,2001.
[7] D. W. Gong, N. N. Qin and X. Y. Sun, “Evolutionary algorithm for optimization problemswith uncertainties and hybrid indices,” Information Sciences, vol. 181, no. 19, pp.4124-4138, 2011.
[8] J. G. Liu, C. H. Lee, F. Yang, H. Wan and R. Uzsoy, “Production planning for semiconductor manufacturing via simulation optimization,” in Proc. Winter Simul. Conf., pp. 230-238, Dec.2009.
[9] Global Environment Fund and Center for Smart Energy, “The Emerging Smart Grid: Investment and Entrepreneurial Potential in the Electric Power Grid of the Future,” Oct.2005.
[10] S. M. Amin and B. F. Wollenberg, “Toward a smart grid: Power delivery for the 21st century,” IEEE Power Energy Mag., vol. 3, no. 5, pp. 34–41, Sep.-Oct. 2005.
[11] C. W. Geillings, “The Smart Grid: Enabling Energy Efficiency and Demand Response”.Boca Raton, FL: CRC Press, 2009.
[12] X. Chen, T. Wei, S. Hu, Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home, IEEE Trans. on Smart Grid, vol. 4, no. 2, June 2013, pp. 932-941..
[13] Y. Ozturk, D. Senthikumar, S. Kumar and G. Lee, “An intelligent home energy management system to improve demand response,” IEEE Trans. on Smart Grid, vol. 4, no. 2, pp. 694-701, June 2013.
[14] A. Zipperer, P. A. Aloise-Young, S. Suryanarayanan, R. Roche, L. Earle, D. Christensan, P. Bauleo and D. Zimmerle, “Electric energy management in the smart home: Perspectives on 4 enabling technologies and consumer behaviour,” Proceedings of the IEEE, vol. 101, no. 11, pp. 2397-2408, Nov. 2013.
[15] SOLAR-PV POWER GENERATION DATA. [Online], http://www.elia.be/en/grid-data/power-generation/Solar-power-generation-data/Graph
[16] Ameren-RealTimePrices[Online],https://www2.ameren.com/RetailEnergy/RealTimePrices
[17] Z. Z. Zhou, Y. S. Ong, M. H. Lim, and B. S. Lee, “Memetic algorithm using multi-surrogates for computationally expensive optimization problems,” Soft Computing, vol. 11, no. 10, pp. 957-971, Aug. 2007.
[18] Thomas Bäck, Hans-Paul Schwefel. “An overview of evolutionary algorithms for parameter optimization”, Evolutionary Computation, vol. 1 no. 1, pp. 1-23, MIT Press, 1993.ocba
[19] David Beasley, David R. Bull, Ralph R. Martin, “An overview of genetic algorithms: Part 1, Fundamentals”, University Computing, vol. 15, no. 2, pp 58-69, 1993.
[20] D. Beasley, D. R. Bull, Ralph R. Martin, “An overview of genetic algorithms: Part 2, Research Topics”, University Computing, vol. 15 no. 4, pp170-181, 1993.
[21] Peter Mooney, Adam Winstanley, “An evolutionary algorithm for multi-criteria path optimization problems”, International Journal of Geographical Information Science, vol. 20, no 4, 2006.
[22] Sanghamitra Bandyopadhyay, Ujjwal Maulik, Debadyuti Roy, “Gene Identification: Classical and Computational Intelligence Approaches”, International Conference on Evolutionary Programming, vol.1447, 1998, pp. 611-616, 1998.

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