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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益(Ray-I Chang) | |
| dc.contributor.author | Yu-Hsin Hung | en |
| dc.contributor.author | 洪鈺欣 | zh_TW |
| dc.date.accessioned | 2021-06-13T08:05:40Z | - |
| dc.date.available | 2014-07-27 | |
| dc.date.copyright | 2011-07-27 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-20 | |
| dc.identifier.citation | [1] S. Y. Lin, “Improving PSO by query-based learning,” M.S. thesis, National Taiwan University, 2007.
[2] B. K. Chen and C. C. Hong, “Optimum operation for a back-pressure cogeneration system under time-of-use rates,” IEEE Transactions on Power System, vol. 11, no. 2, pp. 1074–1082, 1996. [3] M. T. Tsay and W. M. Lin, “Application of evolutionary programming to optimal operational strategy cogeneration system under time-of-use rates,” Electrical Power Energy System, vol. 22, pp. 367–373, 2000. [4] G. Granelli, M. Montagna, and F. Zanellini, “A genetic algorithm based procedure to optimize system topology against parallel flows,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp.338 – 340, 2006. [5] S. C. Chu, P. W. Tsai, “Computational intelligence based on the behavior of cats,” International Journal of Innovative Computing, vol. 3, no. 1, 2007. [6] J. C. Hwang, J. C. Chen, J.S. Pan, “CSO and PSO to solve optimal contract capacity for high tension customers,” Power Electronics and Drive Systems International Conference on Taipei, pp246 – 251, 2009. [7] T. A. Victoire, A. E. Jeyakumar, “Hybrid PSO–SQP for economic dispatch with valve-point effect,” Electric Power Systems Research vol.71, pp. 51–59, 2004. [8] S. Farag, A. Baiyat, and T. Cheng, “Economic load dispatch multi-objective optimization procedures using linear programming techniques,” IEEE Transaction on Power System, vol. 10, no. 2, pp. 731–738, 1995. [9] R. Jabr, A. Coonick, and B. Cory, “A homogeneous linear programming algorithm for the security constrained economic dispatch problem,” IEEE Transaction on Power System, vol. 15, no. 3, pp. 930–936, 2000. [10] I. Habiballah and M. Irving, “Observability analysis for state estimation using linear programming,” IEEE Proceedings Generation, Transmission and Distribution, vol. 148, no. 2, pp. 142–145, Mar. 2001. [11] S. L. Wen, Y. T. Chen, T.H. Wu, “Optimization for ice-storage air-conditioning system using particle swarm algorithm,” 2010. [12] D. Chattopadhyay, “A practical maintenance scheduling program mathematical model and case study,” IEEE Transaction on Power System, vol.13, no. 4, pp. 1475–1480, Nov. 1998. [13] X. Lin, A. David, and C. Yu, “Reactive power optimization with voltage stability consideration in power market systems,” IEEE Proceedings Generation, Transmission and Distribution, vol. 150, no. 3, pp. 305–310, 2003. [14] C. Lehmkoster, “Security constrained optimal power flow for an economical operation of FACTS-devices in liberalized energy markets,” IEEE Transaction on Power System, vol. 17, no. 2, pp. 603–608, 2002. [15] W. Si1, H. Ogai1, T. Li1, M. Ogawa1,K. Hirai, and H. Takahashi , “Energy Saving System for Office Lighting by Using PSO and ZigBee Network,” 2010 [16] G. Shrestha, B. Pokharel, T. Tek, and S. Fleten, “Medium term power planning with bilateral contracts,” IEEE Transactions on Power Systems, vol. 20, no.2, pp. 627–633, 2005. [17] A. Escobar, R. Gallego, and R. Romero, “Multistage and coordinated planning of the expansion of transmission systems,” IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 735–744, 2004. [18] E. Aponte and J. Nelson, “Time optimal load shedding for distributed power systems,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 269–277, 2006. [19] A. Conejo, N. Alguacil, and G. Fernandez, “Allocation of the cost of transmission losses using a radial equivalent network,” IEEE Transactions on Power Systems, vol. 18, no. 4, pp. 1353–1358, 2003. [20] D. Pudjianto, S. Ahmed, and G. Strbac, “Allocation of VAr support using LP and NLP based optimal power flows,” IEEE Proceedings Generation, Transmission and Distribution, vol. 149, no. 4, pp. 377–383, 2002. [21] R. I. Chang, C. Y. Lo, W. D. Su, J. C. Wang, “Query-based learning decision tree and its applications in Data Mining,” IEEE Transactions on Neural Networks, vol.14, no.6, pp.1437-1448, 2003. [22] S. Matthew, and S. Terence, “Breeding swarms: a GA/PSO hybrid, Genetic and Evolutionary Computation,” pp. 161 – 168, 2005. [23] J. Hwang, J. Choi, S. Oh, R. Marks II, “Query-based learning applied to partially trained multilayer perceptrons,” IEEE Transactions on Neural Networks, vol. 2, pp.131-136, 1991. [24] R. I. Chang, and P. Y. Hsiao, “Unsupervised query-based learning of neural networks using selective-attention and self-regulation,” IEEE Transactions on Neural Networks, vol.18, no.2, pp.205-217, 1997. [25] R. I. Chang, L. B. Liang, W. D. Su, J. C. Wang, and J. S. Kouh, “Intrusion detection by Back-propagation Neural Networks with sample-query and attribute- query,” International Journal of Computational Intelligence Research, 2006. [26] E. B. Baum, “Neural-net algorithms that learn in polynomial time from examples and queries,” IEEE Transactions on Neural Networks, Vol. 1, pp.5-19, 1991. [27] W. M. Lin, F. S. Cheng, and M. T. Tsay, “An improved Tabu search for economic dispatch with multiple minima,” IEEE Transactions on Power Systems, vol. 17, no. 1, pp.108-112, 2002. [28] Y. F. Chen, T. Y. Lee, T. Y. Yang, “A study on optimizing the power-contract：Based example on some resort,” Hsiuping Journal.vol.13, pp.251-266, 2006. [29] S. X. Yang and N. Li, “Power demand forecast based on optimized Neural Networks by improved genetic algorithm,” International Conference on Machine Learning and Cybernetics, pp.2877 – 2881, 2006. [30] D. J. Wu, P. Kleindorfer, J. E. Zhang, “Optimal bidding and contracting strategies in the deregulated electric power market: part II,” Proceedings of the 34th Annual Hawaii International Conference , 2011 [31] Z. L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Transactions on Power Systems, vol. 18, no. 3, pp.1187-1195, 2003. [32] C. L. Chen, N. Chen, “Multi-area economic generation and reserve dispatch,” Proceedings IEEE PICA Conference, pp. 368-373, 2001. [33] C. L. Chen, and N. Chen, “Direct search method for solving economic dispatch problem considering transmission capacity constraints,” IEEE Transactions on Power Systems, vol. 16, no. 4, pp.764-769, 2001. [34] S. H. Karaki, R. B. Chedid, and R. Ramadan, “Probabilistic production costing of diesel-wind energy conversion systems,” IEEE Transactions on. Energy Conversion, vol.15, no. 3, pp. 284-289, 2000. [35] J. Tang, R. Zhen, Y. Tao, “Research on market model for the generation side of the power market,” IEEE Power Engineering Society General Meeting, pp.1-5, 2006. [36] J. P. Yang, Y. J. Chen, C. Y. Chang, H. C Huang, S. N. Tsai, R. C. Hwang, “An AI estimator of electric contract capacity for CATV system based on QNN model,” Fourth International Conference on Innovative Computing, Information and Control, 2009. [37] S. C. Chu, J. F. Roddick and J. S. Pan, “Ant colony system with communication strategies,” Information Sciences, vol.167, pp.63-76, 2004. [38] D. Marco, B. Mauro, S. Thomas, “Ant colony optimization,” IEEE Computational Intelligence Magazine, vol.1, no.4, pp. 28 -39, 2006. [39] G. H. Shakouri, M. Rastad, J. Nazarzadeh, “A hybrid nonlinear model for the annual maximum simultaneous electric power demand,” IEEE Transactions on Power Systems, vol. 21, , pp.1069 – 1078, 2006. [40] P. H. Weng, Y. J. Chen, H. C. Huang, R. C. Hwang, “Power load forecasting by neural models,' Engineering Intelligent Systems For Electrical Engineering and Communications,” vol. 15, no. 1, pp. 33-39, 2007. [41] M. Abdel, Y.L. and M.A. Abido, “Optimal multi-objective design of robust power system stabilizers using genetic algorithms,” IEEE Transactions on Power Systems, vol.18, no.3, pp. 1125-1132, 2003. [42] G. Levitin, A. Lisnianski, H.B. Haim, and D. Elmakis, “Genetic algorithm and universal generating function technique for solving problems of power system reliability optimization,” Electric Utility Deregulation and Restructuring and Power Technologies International Conference, 2003. [43] J. H. Houwelingen and W. F. Raaij, “The Effect of Goal-Setting and Daily Electronic Feedback on In-Home Energy Use,” The Journal of Consumer Research, vol. 16, no. 1, 1993. [44] J. M. Saphores, H. Nixon, A. A. Shapiro, “Household willingness to recycle electronic waste: an application to California,” Environment and Behavior, vol. 38, pp. 183-208, 2006. [45] S. Chakraborty, M. D. Weiss, M. G. Simoes, “Distributed intelligent energy management system for a single-phase high-frequency AC micro-grid,” IEEE Transaction on Industrial Electronics, vol. 54, no. 1, pp. 97-109, 2007. [46] L. Yang, “Automatic calibration of a rainfall–runoff model using a fast and elitist multi-objective particle swarm algorithm,” Expert Systems with Applications, vol.36, no.5, pp. 9533-9538, 2009. [47] M. Daniel, G. Helmut, and S. Ulf, “A successive approach to compute the bounded Pareto Front of practical multi-objective optimization problems,” Journal on Optimization, vol. 20, no. 2, pp. 915-934, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/36562 | - |
| dc.description.abstract | 近年來綠色環保議題廣泛受到重視，有效率地使用能源成為永續經營之關鍵，資訊科技迅速進步，生活品質要求度高，伴隨著電能消耗量大幅提升，透過負載管理能夠改善當前用電供需端之現況，有鑑於此，電力公司提出簽訂用電契約來進行輸電端之負載管理，企業等大型用戶與電力公司簽訂用電契約，電力公司可以根據契約量預先進行電力調度及排程，減少電能之浪費及提升電力服務品質，用戶可以藉由簽訂契約，了解自身所需的電量並達到有效用電。訂定適當的契約量能夠使用戶有效地節省用電成本，過低的契約量造成罰款問題，導致用電成本提升；簽訂過高的契約量，雖可避免罰款但也因此造成不必要的電能浪費，亦造成用電成本增加。合理地評估負載量及契約量可以解決上述問題，因此本研究提出負載預測及契約訂定最佳化，其目的在於幫助用戶評估適當的契約量，維持用戶穩定用電品質下，並節省用電成本。由於粒子群演算法具備解空間資訊之多樣性，以及不受限於資料量限制等特性，我們透過粒子群演算法搭配時間序列模型進行負載預測，並透過隨機模擬機制進行調控，預測下一年可能的負載量，在最佳化模組部分，藉由詢問式學習概念提升粒子群演算法效能[1] 並計算全年契約量。本研究以實際案例進行實驗，其結果指出，本研究方法在負載預測中達約90%之準確度，在各產業領域中，在公營單位、商業、製造業及服務業準確度為90%、92%、90%、85%；根據實驗結果指出可幫助用戶在公營單位、商業、製造業及服務業，分別節省$$195,374、$4,031、$30,978、$39,905之用電成本。 | zh_TW |
| dc.description.abstract | In Taiwan, most industrial and commercial enterprises sign power contracts with Taiwan Power Company. Problems occur when deciding the capacity in contracts: the high power capacity leads to increase of total electronic consuming cost. However, if the power capacity is set low, consumers run the risk of high penalty when the actual consumption exceed. The aim of this thesis is to optimize power demand for Taiwanese industries through the model of forecast and optimization. This thesis presents a new combination method by using particle swarm optimization (PSO) to forecast the load capacity, and control uncertainty of forecasting with stochastic simulation. Then optimize the capacity of contract with the improved particle swarm optimization by query based learning (QBLPSO) [1] algorithm. There are two main purposes in this thesis. First, the proposed method will be compared with the other methods, and we analyze separately the forecast and optimization. Second, we make decision analysis framework for determining the optimal power contract capacity and an empirical study in real cases, which included the industry, the commerce. The load forecast has about 90% of accuracy in government units, 92% of accuracy in commerce, 90% of accuracy in manufacturing industry, and 85% of accuracy in service industry. And optimization model help user to save about $195,374 in government units, $4,031 in commerce, $30,978 in manufacturing industry, and $39,905 in service industry. Therefore, the result of experiment explain that this proposed method can help user efficiently to make appropriate contract capacity. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T08:05:40Z (GMT). No. of bitstreams: 1 ntu-100-R98525039-1.pdf: 753124 bytes, checksum: 317d679b98818c36007cedf9c4b375aa (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Objective and Contribution 6 1.3 Thesis Organization 7 Chapter 2 Literature review 8 2.1 Load forecast method 8 2.2 Optimization strategy in the power system 9 2.2.1 Particle swarm optimization (PSO) 11 2.2.2 Query based learning (QBL) 12 Chapter 3 Methodology 13 3.1 Load forecast model and simulation mechanism 14 3.1.1 Load forecast model 14 3.1.2 Stochastic simulation mechanism 17 3.2 Contract optimization model 20 3.2.1 Parameters and decision variables 20 3.2.2 The Objective Function of Optimal Demand 21 3.2.3 Contract optimization model 23 Chapter 4 Performance evaluation 25 4.1 Experimental setting 25 4.2 Performance analysis of forecast methods 26 4.2.1 The procedure of load forecast experiment 27 4.2.2 The forecast results analysis in real case 29 4.3 Optimization performance 31 4.3.1 The procedure of load forecast experiment 32 4.3.2 The optimization results analysis in the real cases 32 4.4 The performance analysis 36 4.4.1 The efficiency evaluation of thesis method 36 4.4.2 The analysis of marginal cost and contract capacity relationship 38 Chapter 5 Conclusion and future work 40 5.1 Conclusion 40 5.2 Future work 41 REFERENCES 43 | |
| dc.language.iso | en | |
| dc.subject | 臺灣電力公司 | zh_TW |
| dc.subject | 契約用電 | zh_TW |
| dc.subject | 時間序列 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | 粒子群演算法 | zh_TW |
| dc.subject | 最佳化 | zh_TW |
| dc.subject | 詢問式學習 | zh_TW |
| dc.subject | Time series | en |
| dc.subject | Query Based Learning. | en |
| dc.subject | Particle Swarm optimization | en |
| dc.subject | Forecasting | en |
| dc.subject | Taiwan Power Company | en |
| dc.subject | Power-Contract | en |
| dc.title | 負載預測與契約用電最佳化之研究 | zh_TW |
| dc.title | Load Prediction and Contract Capacity Optimization Research | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 丁肇隆(Chao-Lung Ting),王家輝(Chia-Hui Wang),林正偉(Jeng-Wei Lin),黃乾綱(Chien-Kang Huang) | |
| dc.subject.keyword | 臺灣電力公司,契約用電,時間序列,預測,粒子群演算法,最佳化,詢問式學習, | zh_TW |
| dc.subject.keyword | Taiwan Power Company,Time series, Power-Contract,Forecasting, Particle Swarm optimization,Query Based Learning., | en |
| dc.relation.page | 48 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-07-20 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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