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研究生:None
研究生(外文):Gaurav
論文名稱:基於技術指標應用深度學習於冰水主機性能預測
論文名稱(外文):Deep Learning for the Performance Prediction in Chiller based on the Technical Indicators
指導教授:張合李文興李文興引用關係
指導教授(外文):CHANG, HOLEE, WEN-SHING
口試委員:張合李文興陳希立
口試委員(外文):CHANG, HOLEE, WEN-SHINGCHEN, SIH-LI
口試日期:2020-06-17
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:機械工程系機電整合碩士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:42
外文關鍵詞:Chiller performance predictionDeep learningenergy consumptionReal time dataset
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To predict accurate energy consumption in a Heating, Ventilation, and Air Conditioning (HVAC) system,it is important to predict the energy consumption of a chiller because it is a major component in a HVAC system. This increase leads to huge burden amongst the electricity distributors. Thus, prediction of future demand for consuming energy in chiller is important for Heating, Ventilation, and Air Conditioning (HVAC) system. Forecasting the consumption of energy needs several attributes. This research presents a Deep Long Short Term Memory (Deep LSTM) for computing the energy consumption in the Chiller. Deep LSTM model trained with different parameters and Deep LSTM was compared with the different machine learning models to validate the supiority of Deep LSTM in chiller energy prediction. An optimal way to perform the chiller energy prediction is to use the real time dataset, as the Deep LSTM classifier is to provide effective performance using the obtained real time dataset. Here, the networks consider the real time dataset for predicting the future consumption of electricity.The Deep LSTM network outperformed other models with minimal MSE of 0.114 and RMSE of 0.338 respectively.
ABSTRACT i
Table of Contents ii
List of Figures iv
List of Tables v
Chapter 1. Introduction 1
1.1 Preface 1
1.2 Literature Survey 1
1.3 Motivations 12
1.4 Challenges 13
Chapter 2. Materials and Methods 14
2.1 Research Outline 14
2.2 Data description 14
2.3 Neural network 16
2.4 Support vector machine 18
2.5 Naïve bayes 19
2.6 K-nearest neighbor 21
2.7 Deep LSTM for energy usage prediction in chillers 23
2.8 Extraction of technical indicators using input real time dataset 23
2.9 Prediction using Deep LSTM 25
Chapter 3. Results and discussions 28
3.1 Experimental Setup 28
3.2 Database description 28
3.3 Evaluation metrics 28
3.3.1. MSE 28
3.3.2 RMSE: 29
3.4 Performance analysis 29
3.5. Competing methods 30
3.6. Comparative analysis 30
Chapter 4. Conclusion 33
Chapter 5. References 34

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