臺灣博碩士論文加值系統

電力是日常生活和工業生產中不可或缺的資源，它關係到國家經濟建設以及各行業的穩定發展；但由於電力資源本身存在不可大量存儲的特性，其生產和消費應同步進行，因此精確的電力負荷預測對於電力公司以及能源部門來說都至關重要，尤其是對短期電力負荷的預測(Short-Term Load Forecasting)。在已有的研究中，主要通過氣候條件，歷史電力負荷變化情況，以及其它一些因素來進行；但在面對資料結構更加複雜多樣化，變化迅速等情況時，各因素之間的依賴關係分析就顯得非常困難，因而無法進行精確且穩定的預測。由此，本文擬提出基於深度學習框架的循環神經網路模型(MTS-RNN)來對短期電力負荷進行分析：我們首先提出了多重時間序列的概念，將歷史電力負荷根據不同的時間步分為連續型的短期序列和長短期序列，以及具有跳躍性時間步的離散型週期性序列和交叉長短期序列；然後採用深度循環神經網路模型對多重的時間序列進行訓練，對單個和多重的時間序列進行組合學習；最後對所提出的模型進行實驗驗證與分析。實驗證明，本文所提出的組合模型具有很高的預測性能，且在同一資料集下優於當前的其它方法。其中，模型最佳的平均绝对值百分誤差(MAPE)是0.71，而其它最佳是1.03。同時，通過實驗我們還發現不同時間序列之間的依賴關係和對短期預測的影響程度，即連續型的短期序列與長短期序列對短期預測具有很好的性能，而離散型的週期性序列和交叉長短期序列對短期預測效果不显著，但將其組合起來通過深度學習的訓練過程又會起到加強的效果，對最終的預測性能有所提升。

Electricity, is an indispensable resource in daily life and industrial production. It is related to the national economic construction and the steady development of various industries. However, the production and consumption of electricity resources should be synchronization due to non-mass storage. It is important that the accurate forecasting of load for both the utility and the energy sector, especially for short-term load forecasting(STLF). In the past research, taking main the climatic conditions, historical power load changes, and other factors into account, while the dependence analysis between the various factors become very difficult when we deal with the more complex and diversified data structures and rapid changes, result to the forecast accuracy and stability not be guaranteed. In this paper, we propose a recurrent neural network model based on the deep learning framework(MTS-RNN) to analyze the short-term load forecasting. We first propose the concept of multiple time series, according to the different time-steps, we divide the historical power load into continuous sequences like short-term series and long short-term series, as well as discrete sequences like cycle series and cross long short-term series with jumping time-steps. Then, a deep recurrent neural network model is used to train the single and multiple time series, and analyze it. Experiments show that the combined model has high forecasting performance presented in this paper and superior to other methods in the same data set. The best mean absolute percent error(MAPE) is 0.71, the other best is 1.03. At the same time, we also found that the relationship of dependency between different time series and which impact for short-term forecasting. Namely, the continues sequences on the short-term forecasting have good results while the discrete sequences consequence is bad. However, it can also strengthen the final performance on short-term load forecasting when we combine them by deep learning train process.

摘要 iii
ABSTRACT v
誌 謝 vii
目錄 ix
表目錄 xii
圖目錄 xiii
第一章 緒論 1
1.1研究背景及意義 1
1.1.1 研究背景 1
1.1.2 研究意義 3
1.2主要研究內容 5
1.2.1電力預測的含義 5
1.2.2電力預測的分類及特性 5
1.2.3電力負荷預測研究現狀 7
1.2.4擬研究的問題及本文解決思路 9
1.2.5主要貢獻 9
1.3本文組織 10
1.4本章小結 11
第二章 相關工作 13
2.1 基於數學統計方法 13
2.2 機器學習方法 15
2.3 深度學習方法 16
2.4本章小結 17
第三章 研究方法 18
3.1 研究方法架構 18
3.2多重時間序列資料生成 19
3.2.1短期序列 19
3.2.2週期性序列 20
3.2.3長短期序列 21
3.2.4交叉長短期序列 21
3.3 深度循環神經網路模型 22
3.3.1簡單循環神經網路 23
3.3.2長短期記憶網路 25
3.3.3門限循環單元網路 27
3.5 RMSProp優化器 28
3.6本章小結 29
第四章 實驗結果 30
4.1 資料集描述 30
4.2 評估方法 31
4.3 單時間序列結果 32
4.3.1 短期序列結果 32
4.3.2 週期序列結果 33
4.3.3 長短期序列結果 34
4.3.4 交叉長短期序列結果 35
4.4 多重時間序列組合結果 37
4.4.1 結合短期序列與週期性序列 37
4.4.2 結合長短期序列與交叉長短期序列 38
4.4.3 結合短期序列與長短期序列 39
4.4.4 結合三種序列結果 40
4.4.5 結合四種序列結果 41
4.5 與其他方法的比較及其他評估指標結果 43
4.6 討論與分析 45
4.8本章小結 46
第五章 結論及未來研究工作 47
5.1 結論 47
5.2 本文存在的不足 47
5.3 未來研究工作 48
參考資料 50

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