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系統識別號 U0026-0108201312145900
論文名稱(中文) 應用支撐向量回歸和模糊推論於光伏電池發電量預測
論文名稱(英文) Forecasting of PV Power Output Based on Support Vector Regression and Fuzzy Inference Approach
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 2
出版年 102
研究生(中文) 白亦翔
研究生(英文) Yi-Shiang Pai
學號 VE6002041
學位類別 碩士
語文別 英文
論文頁數 49頁
口試委員 指導教授-楊宏澤
口試委員-林惠民
口試委員-張忠良
口試委員-黃慶連
口試委員-蒲冠志
中文關鍵字 模糊推論  預測  光伏電池  支撐向量回歸 
英文關鍵字 fuzzy inference  forecasting  photovoltaic systems  support vector regression 
學科別分類
中文摘要 本文使用支撐向量回歸和模糊推論方法,預測未來一天的光伏電池發電量。論文中使用的支撐向量回歸已經成功的運用在分類和回歸分析上,支撐向量機則使用最佳超平面來得到線性或非線性的高維特徵空間。本文提出的預測方法在訓練部分,支撐向量回歸用來訓練收集的歷史資料,包含最高溫度、降雨機率和相似時且相同天氣的照度,訓練資料的分類則採用模糊推論方法。在預測部分,根據台灣中央氣象局提供的未來一天天氣資料經由模糊推論方法,選擇適合的已訓練完成之預測模式。為測試提出的方法,本文實際採用台灣中央氣象局一年完整天候資料,以測試一光伏電池發電系統之發電量,並以實際結果比較其預測準確度。數值結果顯示本文所提的預測方法比起單一支撐向量機方法和傳統的類神經網路方法有較高的預測準確率。
英文摘要 This thesis uses support vector regression (SVR) and fuzzy inference method for one-day ahead forecasting of photovoltaic (PV) power output. SVR employed in this thesis has been successfully applied to data classification and regression analysis. It uses the best hyperplane to extract features from linear or nonlinear data. In the training stage, the SVR is trained by using the collected input data for temperature, probability of precipitation, solar irradiance of defined similar hours, which are classified via fuzzy inference method. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. This thesis uses one-year weather information collected from TCWB to test the PV power forecasting. The comparison with the actual data is used to verify the accuracy. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVM method and traditional ANN method.
論文目次 摘要...i
ABSTRACT...ii
誌謝...iii
Table of Contents...iv
List of Figures...vi
List of Tables...viii
Chapter 1. INTRODUCTION...1
1.1 Backgrounds and Motivations...1
1.2 Review of Literature...2
1.2.1 Indirect Forecasting Methods...2
1.2.2 Direct Forecasting Methods...3
1.3 Research Objective and Methods...4
1.4 Contributions of the Thesis...4
1.5 Organization of the Thesis...5
Chapter 2. CHARACTERISTICS OF PV POWER GENERATION...6
2.1 Introduction...6
2.2 Mathematical Models of PV Module...6
Chapter 3. THE PROPOSED FORECASTING METHOD...11
3.1 Introduction...11
3.2 Proposed PV Power Forecasting Method...11
3.3 Support Vector Regression...15
3.4 Fuzzy Inference Method...19
Chapter 4. SIMULATION RESULTS...27
4.1 Introduction...27
4.2 Forecasting System Evaluation...27
4.2.1 Evaluation Indices...27
4.2.2 Test Bench of the Proposed Forecasting Method...29
4.3 Comparison of Different Forecasting Algorithms...30
Chapter 5. CONCLUSION AND FUTURE PROSPECTS...44
5.1 Conclusion...44
5.2 Future Prospects...45
REFERENCES...46
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