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研究生： 顧芷瑄
Ku, Chih-Hsuan
論文名稱： 探討能源決策管理: 應用機器學習於空氣汙染預測之研究
A Study on Energy Decision-Making: Machine Learning Approaches for Air Pollution Forecasting
指導教授： 廖崇碩
Liao, Chung-Shou
口試委員: 侯建良
Hou, Jiang-Liang
林春成
Lin, Chun-Cheng
學位類別： 碩士
Master
系所名稱： 工學院 - 工業工程與工程管理學系
Department of Industrial Engineering and Engineering Management
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 36
中文關鍵詞： 能源管理 、空氣污染 、預測模型 、支援向量機 、隱馬可夫模型
外文關鍵詞： Energy Management, Air Pollution, Forecast Model, SVM, HMM
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    • 近年來空氣污染一直是大家關注的議題，空氣污染嚴重地影響了人們的生活，以及對人體造成各種不同的危害。然而考量到現實層面，我們很難在空氣品質和經濟發展之中取得平衡。本研究探討了近年來的能源決策議題，並且針對台灣中部的空氣污染進行預測。
    根據行政院環境保護署提供的PM2.5數值與相關的化學及氣象因子等長期時間序列 (time-series) 資料，我們使用了幾種不同類型的機器學習模型去預測PM2.5之濃度，包含了監督式學習的支援向量機 (SVM)、非監督式學習的隱馬可夫模型 (HMM) 及自迴歸隱馬可夫模型 (AR-HMM)。其中，由於自迴歸隱馬可夫模型在觀察值中彼此有相依的關係存在，此結構符合本研究的觀察值之時間序列資料型態，因此相較於其他的機器學習模型，使用自迴歸隱馬可夫模型在PM2.5的濃度預測上擁有較好的預測表現。本研究針對台灣中部地區進行研究，實驗結果顯示了模型的有效性，並提供政府其資訊，以利制定能源政策。

    In recent decades, the air quality issue has caught everyone’s attention and become a significant problem for everybody. It has influenced human living and brought a variety of risks to the health of people. However, it is always difficult to balance air quality and economic development. In this study, we consider the recent debate on energy policy making and investigate the forecast of air pollution in Central Taiwan.
    Based on long-term time-series past data of PM2.5 and relevant chemical and meteorological factors, we use several different types of popular machine learning approaches for predicting the concentration levels of PM2.5. In particular, the autoregressive hidden Markov model (AR-HMM), which admits the existence of dependency between time-series observations, has a relatively better prediction performance. The empirical studies in Taichung area, Taiwan demonstrate the effectiveness of the model, which can be used to assist the government for setting appropriate energy policies.

    摘要 I Abstract II 致謝 III Contents IV List of Figures and Tables V 1. Introduction 1 1.1 Background 1 1.2 Literature Review 4 1.3 Objective 5 1.4 Research Structure 6 2. Preliminary and Terminology 7 2.1 Support Vector Machine 7 2.1.1 Support Hyperplane: Linear Separable 8 2.1.2 Support Hyperplane: Nonlinear Separable 9 2.2 Hidden Markov Model 10 2.2.1 Three Problems of Hidden Markov Model 11 2.2.2 Solutions to Each Problem 12 2.3 Autoregressive Hidden Markov Model 15 3. Methodology and Discussion 17 3.1 Methodology 17 3.2 Discussion on Factors 20 3.2.1 Data Characteristics and PM2.5 Trends 20 3.2.2 Factors Selection 24 3.2.2.1 Basic Chemical Precursors and Meteorological Factors 24 3.2.2.2 The Correlations Between Each Factor 26 3.2.2.3 The Data of PM2.5 in China 27 3.2.2.4 Thermal Power Stations 28 3.2.3 Observations Summary 30 4. Results 31 5. Conclusion 34 Reference 35

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