本文選題：時間序列 + 稀疏表示��； 參考：《蘭州交通大學》2017年碩士論文

【摘要】：近年來,機器學習算法在時間序列預測方面的應用逐漸受到了國內外學者的廣泛關注。SR(Sparse Representation,稀疏表示)是一種典型的稀疏建模機器學習方法,作為一種不同于以往方法的基于內存的建模方法已成為具有重要理論與應用價值的研究熱點。尤其是隨著風力發(fā)電技術的日益成熟,并網風電場規(guī)模不斷增加,風力發(fā)電對電網的影響越來越顯著,風電功率時間序列的精確預測對于電力系統(tǒng)的發(fā)展規(guī)劃意義重大。目前基于神經網絡、SVM(Support Vector Machine,支持向量機)等方法的預測模型具有網絡結構參數難以確定,泛化能力有限等不足。因此,探究稀疏建模方法在風電功率時間序列預測中應用具有重要意義。本文主要研究了稀疏建模方法在混沌時間序列預測和風電功率時間序列預測方面的應用,以滿足現實應用對預測精度的要求,為基于內存的機器學習方法在時間序列預測方面的應用提供了新的思路。本文的主要研究內容包括如下幾個方面:(1)研究了SR的基本理論,探究其中基于貪婪算法和松弛算法的兩類稀疏向量求解思路,以及超完備字典的構造算法的基本原理和算法實現。(2)將SR方法引入混沌時間序列預測模型,通過將時間序列輸入數據的分解重構為超完備字典和稀疏向量的乘積形式,以提取歷史序列中的隱含信息。并將求解得到的稀疏向量和輸出數據代入SVM(Support Vector Machine,支持向量機)方法中,建立SR-SVM組合預測模型,并在基準混沌時間序列中與單一SVM等方法進行對比,驗證方法的可行性。(3)提出一類基于自適應數據字典的稀疏編碼預測模型,將歷史時間序列數據的輸入輸出數據構建以原子形式分別構成輸入和輸出字典,組成字典對;再針對待預測的時延輸入數據向量,直接使用稀疏編碼方法借助字典求得稀疏向量,即可由輸出字典與稀疏向量的內積求得待預測值。與此同時,還考慮了字典的自適應更新策略,以實現在線預測,進一步提高精度。將所提出方法分別應用于混沌時間序列預測以及不同地區(qū)的短期風電功率直接和間接預測中,通過與現有方法在同等條件下的對比,驗證方法的有效性。
[Abstract]:In recent years, the application of machine learning algorithm in time series prediction has been paid more and more attention by scholars at home and abroad. SRS parse representation (sparse representation) is a typical sparse modeling machine learning method. As a kind of memory based modeling method, which is different from previous methods, it has become an important research hotspot in theory and application. Especially with the development of wind power generation technology the scale of grid-connected wind farm is increasing and the influence of wind power generation on power grid is becoming more and more significant. The accurate prediction of wind power time series is of great significance for the development planning of power system. At present, the prediction models based on neural network support Vector machine (SVM) have some disadvantages, such as difficult to determine the network structure parameters, limited generalization ability and so on. Therefore, it is important to explore the application of sparse modeling method in wind power time series prediction. This paper mainly studies the application of sparse modeling method in the prediction of chaotic time series and wind power time series, in order to meet the requirement of prediction accuracy in practical applications. It provides a new idea for the application of memory-based machine learning in time series prediction. The main contents of this paper are as follows: 1) the basic theory of SR is studied, and two kinds of sparse vector solutions based on greedy algorithm and relaxation algorithm are explored. And the basic principle and algorithm realization of the construction algorithm of the supercomplete dictionary. The SR method is introduced into the chaotic time series prediction model, and the decomposition of the input data of the time series is reconstructed into the product form of the supercomplete dictionary and sparse vector. To extract hidden information from a historical sequence. The sparse vector and output data obtained from the solution are substituted into the SVM(Support Vector Machine (support Vector Machine) method, and the SR-SVM combination prediction model is established, and compared with the single SVM method in the benchmark chaotic time series. The feasibility of the method is verified. (3) A sparse coding prediction model based on adaptive data dictionary is proposed. The input and output data of historical time series data are constructed into an input and output dictionaries in atomic form to form dictionary pairs. For the delay input data vector to be predicted, the sparse vector can be obtained directly by using the sparse coding method, and the predicted value can be obtained from the inner product of the output dictionary and the sparse vector. At the same time, the adaptive updating strategy of dictionary is considered to realize online prediction and improve accuracy. The proposed method is applied to the prediction of chaotic time series and the direct and indirect prediction of short-term wind power in different regions. The validity of the proposed method is verified by comparing with the existing methods under the same conditions.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM614

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本文編號：1976487