The prediction in manufacturing industry is an important trend for flexible manufacturing system (FMS), saving energy and carbon reduction. In theory, maintains method of this paper breach the traditional method to analyze energy usage. It can improve rate of loading of energy in factory by using deep learning method to predict. It can reduce over-used energy and provide an advice for manufacture order at energy.
This paper builds an architecture for predict energy consumption in factory. In factory, energy consumption has two parts. One is office equipment and other is production line. Therefore, the research is directed at energy consumption in offices and production lines and discuss by analyzing the relationship between weather, production work order observation and equipment.
The research method has four steps. First, collecting data in a factory about the usage of the office and information about production. Second, preprocessing the collected data from the factory and merging the data from the office, production line and weather for module training and predicting. Third, using the deep learning module as a forecasting module to predict the consumption in the factory. The thesis uses five deep learning modules and compared each module at a different time length. The thesis uses four estimated methods to estimate the performance of module forecasting and will choose one module that has the best performance to predict consumption of manufacturers. After analyzing each variable, the results show the hybrid module (CNN-LSTM) at time length 30 has the best performance in forecasting energy consumption manufacturers. Therefore, the thesis recommends CNN-LSTM as the module to predict the direction of energy consumption in manufacturers. The module CNN-LSTM predicts the result on Root Mean Squared Error, Absolute Mean Error, Percentage Absolute Mean Error and R Squared is 2.7KW2, 24.2KW, 0.63% and 0.92. Last, the thesis uses estimated results to choose a module to predict the energy consumption in manufacturing.

摘要 ........................i
Abstract.........................iii
誌謝 .........................v
目錄 .........................vi
表目錄 .........................viii
圖目錄 .........................ix
符號 .........................xi
第一章 緒論.....................1
1.1 研究背景與動機 .........1
1.2 研究方法 .................2
1.3 研究流程 .................3
1.4 論文章節 .................4
第二章 文獻探討 .................5
2.1 彈性製造 .................5
2.2 電力 .................6
2.3 用電預測 .................7
2.3.1 容量警示系統 .........8
2.3.2 預測場合 .................8
2.4 深度學習模型 .........9
2.4.1 回歸法 .................9
2.4.1.1 多層感知器 ........10
2.4.1.2 卷積神經網路 ........12
2.4.2 時間序列法 ........16
2.4.2.1 長時間記憶神經網路 ......16
2.4.2.2 注意力機制 ........18
2.4.2.3 混合模型 ................20
2.5 本章小節 ................21
第三章 電力模型預測模式建 ......22
3.1 資料預處理 ........23
3.2 Min-Max正規化 ........23
3.3 資料融合 ................24
3.4 深度學習預測 ........25
3.4.1 多層感知器 ........25
3.4.2 卷積神經網路 ........26
3.4.3 長短期記憶神經網路 .....27
3.4.4 LSTM注意力機制 ........28
3.4.5 混合模型 ................29
第四章 電力模型預測建構實作 ....31
4.1 Python ................31
4.2 Keras ................31
4.3 模擬用電運行 ........32
4.4 資料預處理 ........35
4.5 資料融合 ................39
4.6 深度學習預測模型 ........40
4.6.1 多層感知器 ........41
4.6.2 CNN ................42
4.6.3 LSTM-NN ................44
4.6.4 注意力機制 ........47
4.6.5 混合模型 ................48
4.7 模型評估 ................51
第五章 模型驗證與討論 ........53
5.1 驗證一 ................54
5.2 驗證二 ................56
結論 ........................59
參考文獻 ........................60
Extended Abstract 64

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