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

【摘要】：近年來,機(jī)器學(xué)習(xí)算法在時間序列預(yù)測方面的應(yīng)用逐漸受到了國內(nèi)外學(xué)者的廣泛關(guān)注。SR(Sparse Representation,稀疏表示)是一種典型的稀疏建模機(jī)器學(xué)習(xí)方法,作為一種不同于以往方法的基于內(nèi)存的建模方法已成為具有重要理論與應(yīng)用價值的研究熱點(diǎn)。尤其是隨著風(fēng)力發(fā)電技術(shù)的日益成熟,并網(wǎng)風(fēng)電場規(guī)模不斷增加,風(fēng)力發(fā)電對電網(wǎng)的影響越來越顯著,風(fēng)電功率時間序列的精確預(yù)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(jīng)網(wǎng)絡(luò)、SVM(Support Vector Machine,支持向量機(jī))等方法的預(yù)測模型具有網(wǎng)絡(luò)結(jié)構(gòu)參數(shù)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(fēng)電功率時間序列預(yù)測中應(yīng)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(yù)測和風(fēng)電功率時間序列預(yù)測方面的應(yīng)用,以滿足現(xiàn)實應(yīng)用對預(yù)測精度的要求,為基于內(nèi)存的機(jī)器學(xué)習(xí)方法在時間序列預(yù)測方面的應(yīng)用提供了新的思路。本文的主要研究內(nèi)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(gòu)造算法的基本原理和算法實現(xiàn)。(2)將SR方法引入混沌時間序列預(yù)測模型,通過將時間序列輸入數(shù)據(jù)的分解重構(gòu)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(shù)據(jù)代入SVM(Support Vector Machine,支持向量機(jī))方法中,建立SR-SVM組合預(yù)測模型,并在基準(zhǔn)混沌時間序列中與單一SVM等方法進(jìn)行對比,驗證方法的可行性。(3)提出一類基于自適應(yīng)數(shù)據(jù)字典的稀疏編碼預(yù)測模型,將歷史時間序列數(shù)據(jù)的輸入輸出數(shù)據(jù)構(gòu)建以原子形式分別構(gòu)成輸入和輸出字典,組成字典對;再針對待預(yù)測的時延輸入數(shù)據(jù)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(nèi)積求得待預(yù)測值。與此同時,還考慮了字典的自適應(yīng)更新策略,以實現(xiàn)在線預(yù)測,進(jìn)一步提高精度。將所提出方法分別應(yīng)用于混沌時間序列預(yù)測以及不同地區(qū)的短期風(fēng)電功率直接和間接預(yù)測中,通過與現(xiàn)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(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李軍;常燕芝;;基于KPCA-KMPMR的短期風(fēng)電功率概率預(yù)測[J];電力自動化設(shè)備;2017年02期

2 嵇艷鞠;徐鵬;趙雪嬌;朱宇;關(guān)珊珊;欒卉;;基于PCA-RBF神經(jīng)網(wǎng)絡(luò)的航空飛行幾何參數(shù)擬合[J];地球物理學(xué)報;2016年04期

3 查雨彤;劉光達(dá);周潤東;張曉楓;牛俊奇;于永;王偉;;EEMD-ICA在功能性近紅外光譜特征信號提取中的應(yīng)用[J];光譜學(xué)與光譜分析;2015年10期

4 楊茂;齊s，

本文編號：1976487