Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers

碩士 === 大葉大學 === 電機工程學系碩士在職專班 === 93 === Due to the increasing public awareness,consumers reject power-equipments to be set near their houses. Building constructor and electrician also try to avoid setting power equipment near consumers’ houses at all cost. Contracted-consumers have a specific power...

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Main Authors: CHENG JEN MING, 鄭哲民
Other Authors: Yung-Nan Hu
Format: Others
Language:zh-TW
Published: 2005
Online Access:http://ndltd.ncl.edu.tw/handle/59861300144086181072
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spelling ndltd-TW-093DYU014420192015-10-13T15:29:17Z http://ndltd.ncl.edu.tw/handle/59861300144086181072 Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers 類神經網路應用於低壓負載契約用戶之研究與分析 CHENG JEN MING 鄭哲民 碩士 大葉大學 電機工程學系碩士在職專班 93 Due to the increasing public awareness,consumers reject power-equipments to be set near their houses. Building constructor and electrician also try to avoid setting power equipment near consumers’ houses at all cost. Contracted-consumers have a specific power requirement,so Engineers usually design and lay equipments in shopping and schooling districts. In order to reject power-equipments,some consumers usually underestimate measure of power-depletion. This will result in some problems, i.e., full load or overload operation, difficulty in equipment maintenance, power quality degradation and life shortage of equipment. The purpose of this thesis is solving aiming at such problem. Two hundred fifty three contracted-consumers, were studied.To obtain influence factor by using regression analysis for consume’s data ,then applying Neural Network to calculate suitable weight . Fifty consumer data were used for evaluating the performance of the proposed method by comparing the estimated and real values. A better way of grouping and network calculation can be done based on the calculated error. This can also act as reference for contracted- consumers . Yung-Nan Hu Seng-Chi Chen 胡永柟 陳盛基 2005 學位論文 ; thesis 83 zh-TW
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language zh-TW
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description 碩士 === 大葉大學 === 電機工程學系碩士在職專班 === 93 === Due to the increasing public awareness,consumers reject power-equipments to be set near their houses. Building constructor and electrician also try to avoid setting power equipment near consumers’ houses at all cost. Contracted-consumers have a specific power requirement,so Engineers usually design and lay equipments in shopping and schooling districts. In order to reject power-equipments,some consumers usually underestimate measure of power-depletion. This will result in some problems, i.e., full load or overload operation, difficulty in equipment maintenance, power quality degradation and life shortage of equipment. The purpose of this thesis is solving aiming at such problem. Two hundred fifty three contracted-consumers, were studied.To obtain influence factor by using regression analysis for consume’s data ,then applying Neural Network to calculate suitable weight . Fifty consumer data were used for evaluating the performance of the proposed method by comparing the estimated and real values. A better way of grouping and network calculation can be done based on the calculated error. This can also act as reference for contracted- consumers .
author2 Yung-Nan Hu
author_facet Yung-Nan Hu
CHENG JEN MING
鄭哲民
author CHENG JEN MING
鄭哲民
spellingShingle CHENG JEN MING
鄭哲民
Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers
author_sort CHENG JEN MING
title Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers
title_short Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers
title_full Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers
title_fullStr Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers
title_full_unstemmed Research and Analysis Applying Neural Network on Low Voltage Contract-Consumers
title_sort research and analysis applying neural network on low voltage contract-consumers
publishDate 2005
url http://ndltd.ncl.edu.tw/handle/59861300144086181072
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