Forecasting Diesel Power Backup using FOA Optimized GRNN and RNN with Autoencoder

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研究生：	邱作娜 Ni Putu Novita Puspa Dewi
論文名稱：	Forecasting Diesel Power Backup using FOA Optimized GRNN and RNN with Autoencoder Forecasting Diesel Power Backup using FOA Optimized GRNN and RNN with Autoencoder
指導教授：	呂永和 Yungho Leu
口試委員:	楊維寧 Wei-Ning Yang 陳雲岫 Yun-Shiow Chen Azhari SN Azhari SN Khabib Mustofa Khabib Mustofa Mardhani Riasetiawan Mardhani Riasetiawan
學位類別：	碩士 Master
系所名稱：	管理學院 - 資訊管理系 Department of Information Management
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 forecasting 、Gaussian Process 、FOAGRNN 、Autoencoder 、GRU 、LSTM
外文關鍵詞：	Electricity forecasting, Gaussian Process, FOAGRNN, Autoencoder, GRU, LSTM
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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.

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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