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研究生： 周寶玉
IRENE KARIJADI
論文名稱： 透過混合深度學習和先進的數據預處理方法提高能源預測準確性
Improving Energy Forecast Accuracy through Hybrid Deep Learning and Advanced Data Preprocessing Methods
指導教授： 周碩彥
Shuo-Yan Chou
郭伯勳
Po-Hsun Kuo
口試委員: 游慧光
Hui-Kuang Yu
許聿靈
Yu-Ling Hsu
林詩偉
Shih-Wei Lin
陳振明
Jen-Ming Chen
學位類別： 博士
Doctor
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2023
畢業學年度： 112
語文別： 英文
論文頁數： 78
外文關鍵詞： data preprocessing, forecasting, deep learning, energy, artificial intelligence
相關次數： 點閱：51  下載：8 
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    • Energy forecasting is paramount in today's world, given the ever-increasing demand for energy across various sectors and its crucial role in daily life. This study explores the integration of machine learning and data decomposition techniques to enhance the accuracy of energy forecasting. While machine learning, particularly deep learning architectures such as Long Short-Term Memory (LSTM), has shown promise in time-series forecasting, real-world energy data often exhibits nonstationary and nonlinear patterns, challenging traditional forecasting methods. To address this, scholars have proposed hybrid approaches incorporating data decomposition techniques to mitigate the effects of non-stationarity and non-linearity. However, the highest frequency components resulting from decomposition remain challenging, leading to forecasting inaccuracies.

    Therefore, to address these limitations, this study introduces two novel hybrid approaches for energy forecasting, each tailored to specific energy applications. The first approach, CEEMDAN-RF-LSTM, focuses on enhancing the precision of predictions for building energy consumption. The second approach, CEEMDAN-EWT-LSTM, is designed for wind power forecasting. Both methods incorporate advanced data decomposition techniques to effectively handle the highest frequency components. These approaches offer a promising avenue for achieving more accurate and reliable energy forecasts, benefiting industries, investors, and governments in making informed decisions and planning for the future. The effectiveness of these proposed approaches is evaluated using actual time series datasets. Finally, the study concludes by discussing the advantages and potential future developments of deep learning and data preprocessing strategies in the context of time series forecasting for energy-related applications.

    ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF TABLES v LIST OF FIGURES vi CHAPTER 1 INTRODUCTION 7 1.1 Background 7 1.2 Research objective and contribution 11 1.3 Scope and limitations 11 1.4 Organization of thesis 12 CHAPTER 2 THEORETICAL BACKGROUND 13 2.1 CEEMDAN 13 2.2 Empirical Wavelet Transform (EWT) 14 2.3 Random Forest (RF) 15 2.4 Long Short-Term Memory (LSTM) 16 CHAPTER 3 BUILDING ENERGY CONSUMPTION FORECASTING 18 3.1 Introduction to Building Energy Consumption Forecasting 18 3.2 Building Energy Consumption Data 20 3.3 Framework of the Proposed Hybrid CEEMDAN-RF-LSTM Approach 22 3.4 Evaluation Metrics for Energy Building Forecasting 23 3.5 Experimental Setting for Energy Building Forecasting 23 3.6 Experimental Results for Energy Building Forecasting 24 3.7 Energy Building Forecasting Summary 30 CHAPTER 4 WIND POWER FORECASTING 31 4.1 Introduction to Wind Power Forecasting 31 4.2 Wind Power Dataset 34 4.3 Structure of the Proposed CEEMDAN-EWT-LSTM Approach 35 4.4 Experimental Setting Wind Power Forecasting 36 4.5 Evaluation Metrics Wind Power Forecasting 37 4.6 Experimental Results Wind Power Forecasting 37 4.7 Wind Power Forecasting Summary 46 CHAPTER 5 COMPARATIVE EVALUATION 47 5.1 Building Energy Consumption Dataset 47 5.2 Wind Power Dataset 53 5.3 Discussion 59 CHAPTER 6 CONCLUSION AND FUTURE RESEARCH 69 6.1 Conclusion 69 6.2 Future Research 70 REFERENCES 71

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