應用學習向量量化於直接負載控制曲線分類系統之研究

Applications of Learning Vector Quantization to Direct Load Control Curves Classification Systems

蔡碧娥(Bi-O Tsai)

指導教授 ： 楊宏澤

中原大學/工學院/電機工程研究所/碩士(2004年)

https://doi.org/10.6840/cycu200400025

摘要

摘　　要
台電公司自民國68年開始實施時間電價以來，陸續推展可停電力、季節電價、中央空調週期性暫停用電、中央空調遙控降載週期性暫停用電以及儲冷式中央空調系統等多項措施，都有不錯成果，但近年來已接近飽和，各項負載管理措施成效亦趨於平緩，在用電量持續顯著成長，但電力供應擴增不易下，針對各項措施進行深入系統式分析將相當重要。
為建構一有效之直接負載控制曲線分類系統，作為深入系統化分析各項負載管理措施的基礎，本文收集89年台電公司夏季實施之直接負載管理措施資料作為測試及訓練資料，其中包含中央空調週期性暫停用電、中央空調遙控降載週期性暫停用電及儲冷式中央空調系統等中央空調管理措施。本研究方法引用統計分析法則建構特徵值，再採用學習向量量化網路進行特徵值篩選偵測，建立分類的辨識模式。本文針對參與用戶負載管理措施之配合，進行直接負載控制曲線分類之研析，其成果除提供電力公司後續進行負載管理措施的績效評估之依據外，亦可供各項負載管理措施改善參考，以提昇各負載用電管理方案的成效，改善電力公司的系統負載狀況。
本文軟體程式係以Excel及MATLAB軟體撰寫特徵值建構與分類系統之計算機程式，用以驗證所提方法之準確性。經由試驗結果顯示，本文所提方法其程式開發容易，且效益顯著，各項負載管制分類辨識至少可達近九成以上，甚至可高達100%。由數值結果發現，對於不同曲線變化類型，本文所提方法成效雖不一，但仍不失為一簡易、節省人力篩選之選擇。
關鍵詞：學習向量量化、特徵擷取、直接負載控制

關鍵字

直接負載控制 ； 學習向量量化 ； 特徵擷取

並列摘要

Abstract
Starting with time of use policies, Taiwan Power Company (TPC) has adopted various strategies of load management since 1979, including interruptible rates, seasonal rates, central air conditioning duty cycling control, ice storage central air conditioning systems, and paging system in central air conditional duty cycling control programs. Performance of various load management strategies had been satisfied, but in recent years the effectiveness seems saturated with little growth. However, with the power demand ever increasing and supply not easily expanding, systematic in-depth analysis of various load management strategies is significant to further upgrade their performance.
As a basis of systematic in-depth analysis of various load management strategies, in this thesis, collected from TPC were the data in 2000 for training and testing an effective classification system of direct load control (DLC) curves. The database includes that for central air conditioning duty cycling control, ice storage central air conditioning systems, and paging system in central air conditional duty cycling control. Diverse statistic analytical numerical rules are combined with the learning vector quantization (LVQ) networks to extract the features of the DLC curves and classify curves accordingly. By dividing the customers into complying with DLC policies or not, results can be provided as the basis of subsequent performance evaluation of the DLC strategies. On the other side, the results can also be used as the references to improve the effectiveness of load management policies and related load factor.
To verify the proposed approaches, software of Excel and MATLAB is employed to derive the features and construct the classification system. The testing results reveal that the computer programs of the proposed approaches can be developed easily with high performance. The classification rates of three DLC strategies can reach nearly 90% above, even as high as 100% for several cases. Though the classification performance varies for different DLC curves, the proposed approaches are simple and manpower-saving ones.
Keywords: Learning Vector Quantization, Feature Extraction, Direct Load Control

並列關鍵字

Feature Extraction ； Direct Load Control ； Learning Vector Quantization

參考文獻

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