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研究生:邱作娜
研究生(外文):Ni Putu Novita Puspa Dewi
論文名稱:Forecasting Diesel Power Backup using FOA Optimized GRNN and RNN with Autoencoder
論文名稱(外文):Forecasting Diesel Power Backup using FOA Optimized GRNN and RNN with Autoencoder
指導教授:呂永和
指導教授(外文):Yungho Leu
口試委員:楊維寧陳雲岫Azhari SNKhabib MustofaMardhani Riasetiawan
口試委員(外文):Wei-Ning YangYun-Shiow ChenAzhari SNKhabib MustofaMardhani Riasetiawan
口試日期:2019-07-12
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:105
中文關鍵詞:Electricity forecastingGaussian ProcessFOAGRNNAutoencoderGRULSTM
外文關鍵詞:Electricity forecastingGaussian ProcessFOAGRNNAutoencoderGRULSTM
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In a developing country like Indonesia, maintaining high quality of electricity power supply is important to the continuous development of the country. This study aims to employ advanced technique of deep learning to forecast the diesel backup power output of a power plant in Indonesia. The hyper-parameter optimization in deep learning is time-consuming. In this study, we used Gaussian optimization process and Fruit Fly Optimization Algorithm (FOA) to optimize the hyper-parameter setting. We compared the performance of General Regresssion Neural Network (GRNN) optimized by FOA and both Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) optimized by Gaussian Process (GP). To improve the prediction accuracy, we used the LSTM autoencoder to encode the input sequence. With the encoded input, both LSTM and GRU offered significant higher prediction accuracy than the ones without input encoding. The experimental results showed that the GRU with Autoencoder and Gaussian process optimization offered the least prediction error as compared to the other prediction models.
In a developing country like Indonesia, maintaining high quality of electricity power supply is important to the continuous development of the country. This study aims to employ advanced technique of deep learning to forecast the diesel backup power output of a power plant in Indonesia. The hyper-parameter optimization in deep learning is time-consuming. In this study, we used Gaussian optimization process and Fruit Fly Optimization Algorithm (FOA) to optimize the hyper-parameter setting. We compared the performance of General Regresssion Neural Network (GRNN) optimized by FOA and both Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) optimized by Gaussian Process (GP). To improve the prediction accuracy, we used the LSTM autoencoder to encode the input sequence. With the encoded input, both LSTM and GRU offered significant higher prediction accuracy than the ones without input encoding. The experimental results showed that the GRU with Autoencoder and Gaussian process optimization offered the least prediction error as compared to the other prediction models.
ABSTRACT i
ACKNOWLEDGEMENT ii
TABLE OF CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vii
LIST OF APPENDIX viii
Chapter 1. Introduction 1
Chapter 2. Literature Review 5
2.1. Related Work 5
2.2. Theoretical Foundation 8
2.2.1. Fruit Fly Optimization Algorithm 8
2.2.2. Bayesian Optimization using Gaussian Process 10
2.2.3. General Regression Neural Networks 11
2.2.4. Long Short-Term Memory 13
2.2.5. Gated Recurrent Unit 16
Chapter 3. Methods 18
3.1. Dataset Description 18
3.2. Data Preprocessing 21
3.2.1. Calculation of Line to Line Voltage 21
3.2.2. Min-Max Normalization 22
3.3. FOAGRNN 23
3.4. RNN Models 27
3.4.1. LSTM and GRU Model 27
3.4.2. Autoencoder 29
3.4.3. RNNs Optimization Process 32
3.4.4. Activation Function 34
Chapter 4. Experimental Result 35
4.1. Experimental Setup 35
4.1.2 FOAGRNN and RNN Models 36
4.1.2 SARIMA Model 41
4.2. Experimental Results 43
4.2.1 Forecasting Performance on FOAGRNN 43
4.2.2 Forecasting Performance on RNN models 47
4.2.3 Performance Comparisons of All Models 52
4.2.4 Performance Comparisons on Other Cases 59
4.2.5 Final Comparison with the Traditional Model 66
Chapter 5. Conclusions and Future Research 69
5.1. Result Summary 69
5.2. Limitation 69
5.3. Future Research 70
REFERENCES x
APPENDIX xiii
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