臺灣博碩士論文加值系統

在現有的冰水系統上，對於冰水主機負載幾乎未特別考量其個別效率，而是採用平均負載法，所以近年來已有研究提出相關演算法來做冰水主機負載分配的最佳化，但是在研究中對於冰水主機耗電模型皆採用線性迴歸分析方式建立，其對於非線性問題尚有準確度較低缺點。
因此，本研究利用類神經網路(Neural Networks；NN)來建立冰水主機運轉耗電量模型，並且利用粒子群演算法(Particle Swarm Optimization；PSO)來進行負載分配最佳化，進而可以達到節約能源目的。
本論文使用一高耗電量案例來做分析，以類神經網路結合粒子群演算法之結果與採用迴歸分析之平均負載法做比較，結果顯示，類神經網路結合粒子群演算法在冰水主機負載最佳分配上，相較平均負載法於不同負載有較佳結果，最低改善節能效率在55%負載下為2.63%，而最高改善節能效率50%負載下為7.8%。因此，本研究結果發現應用類神經網路在建立冰水主機耗電模型上收斂速度快，再結合粒子群演算法於冰水主機負載分配最佳化上準確度又高，因此可以運用在空調系統與其它相關最佳化問題上。

On the existing chiller water system, has not considered its individual efficiencies of chiller then uses the mean load method. Therefore, the recent years had the research to propose that the related calculating method to make the optimization of chiller loading, but power consumed model in the research regarding to use the linear regression analysis mode establishment, it still had the accuracy low shortcoming in regarding the non-linear problem.
This dissertation used neural networks (NN) to build the model of power consumption of the chiller and use particle swarm optimization (PSO) algorithm to optimize the chiller loading for minimizing power consumption. We obtained 2.63% power saving on 55% chiller partial load rate (PLR) and 7.8% power saving on 50% PLR after analysis and comparison with the linear regression (LR) and equal loading distribution (ELD) methods. Therefore, the NNPSO method solved the problem with fast convergence on optimal chiller load (OCL), and produced highly accurate results within a short timeframe. The proposed approach can be applied to air-conditioning systems and other related optimization problems.

摘 要 i
ABSTRACT ii
目 錄 vi
表目錄viii
圖目錄ix
第一章 緒論 1
1.1前言 1
1.2文獻回顧 3
1.3研究動機與目的 6
1.4論文架構 9
第二章 中央冰水系統說明 10
2.1冰水系統架構 10
2.2冰水系統運作原理 14
2.3冰水系統供應方式 16
2.4 冰水主機效率 19
第三章 數學模式之建立 28
3.1冰水主機耗電模型 28
3.2類神經網路 30
3.3粒子群演算法 38
第四章 應用類神經網路與粒子群演算法於冰水主機最佳負載分配 44
4.1案例介紹 44
4.2類神經網路於冰水主機耗電模型分析問題之流程 45
4.3粒子群演算法於冰水主機負載分配問題之流程 48
4.4類神經網路耗電模型分析 50
第五章 實驗結果與討論 56
5.1平均負載法耗電模型分析 56
5.2類神經網路與迴歸分析結果比較 60
5.3類神經網路結合粒子群演算法最佳化分析結果 60
5.4迴歸分析結合平均負載法與類神經網路結合粒子群演算法結果比較 62
5.5迴歸分析結合粒子群演算法最佳化分析結果 64
5.6迴歸分析結合平均負載法與迴歸分析結合粒子群演算法結果比較 66
第六章 結論與建議 69
6.1結論 69
6.2建議 71
參考文獻 72
附錄
A 類神經網路建立冰水主機耗電模型與粒子群最佳化演算法模擬參數 77
符號彙編 78

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