發(fā)布時(shí)間：2018-04-19 01:16

  本文選題：風(fēng)電功率預(yù)測 + BP神經(jīng)網(wǎng)絡(luò)��； 參考：《太原理工大學(xué)》2014年碩士論文

【摘要】：隨著世界經(jīng)濟(jì)和社會的快速發(fā)展,傳統(tǒng)的能源結(jié)構(gòu)已經(jīng)不能滿足人們對能源的需求,人們的目光已經(jīng)從傳統(tǒng)的化石能源轉(zhuǎn)移到可再生清潔能源上來,而風(fēng)力發(fā)電是所有可再生能源中開發(fā)程度最高的。隨著國內(nèi)外風(fēng)電裝機(jī)容量的快速增長,風(fēng)力發(fā)電產(chǎn)業(yè)正面臨著風(fēng)電并網(wǎng)、風(fēng)機(jī)維護(hù)以及提高風(fēng)能利用率等問題,對風(fēng)電的合理、安全、有效的利用越來越受到人們的重視,風(fēng)電功率的短期預(yù)測能夠有效地解決這些問題。本文綜述了國內(nèi)外風(fēng)力發(fā)電的發(fā)展現(xiàn)狀,風(fēng)電功率預(yù)測技術(shù)的研究現(xiàn)狀、基本原理及預(yù)測方法,以及國內(nèi)外已開發(fā)運(yùn)行的風(fēng)電功率預(yù)測系統(tǒng)。 風(fēng)電功率短期預(yù)測的誤差主要是由內(nèi)在隨機(jī)性因素和外在隨機(jī)性因素造成的。內(nèi)在隨機(jī)性因素是指預(yù)測系統(tǒng)本身存在缺陷或者不完善,外在隨機(jī)性因素是指系統(tǒng)輸入的數(shù)據(jù)不完善或者輸入數(shù)據(jù)本身存在誤差。本文系統(tǒng)的、全面地分析了隨機(jī)性因素對短期風(fēng)電功率預(yù)測帶來的影響,通過改進(jìn)以及設(shè)計(jì)新的預(yù)測系統(tǒng)來解決在風(fēng)電功率短期預(yù)測中隨機(jī)性因素所帶來誤差的問題。 針對內(nèi)在隨機(jī)性因素問題,本文建立了BP神經(jīng)網(wǎng)絡(luò)短期風(fēng)電功率預(yù)測模型,探討了BP建模過程中參數(shù)的確定以及隱含層數(shù)的選取,最終得到了最優(yōu)的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型。仿真結(jié)果顯示BP模型的預(yù)測精度和穩(wěn)定性都比較差。針對BP神經(jīng)網(wǎng)絡(luò)的易陷入局部極小值、穩(wěn)定性差的問題,建立了通過遺傳算法和模擬退火算法改進(jìn)BP模型的GASABP短期風(fēng)電功率預(yù)測模型。仿真結(jié)果顯示預(yù)測系統(tǒng)的預(yù)測精度和穩(wěn)定性有了明顯的提高,有效的解決了局部極值的問題。 針對傳統(tǒng)的機(jī)器學(xué)習(xí)理論的局限性,建立了基于統(tǒng)計(jì)學(xué)習(xí)理論的支持向量機(jī)預(yù)測模型,對預(yù)測模型的參數(shù)的選取采用交叉驗(yàn)證網(wǎng)格搜索法。仿真結(jié)果顯示支持向量機(jī)預(yù)測模型的預(yù)測精度明顯高于GASABP模型。通過對三種模型的對比顯示,經(jīng)過不斷優(yōu)化預(yù)測模型,能夠有效的降低風(fēng)電功率預(yù)測過程中內(nèi)在隨機(jī)性因素對風(fēng)電功率預(yù)測精度的影響。 針對外在隨機(jī)性因素問題,上述幾個(gè)預(yù)測模型在對風(fēng)電功率的影響因素的確定沒有統(tǒng)一的指導(dǎo)原則,需要人為按照經(jīng)驗(yàn)來確定,導(dǎo)致確定的影響因素不完善。此外,由于國內(nèi)風(fēng)電場沒有建立完善和精確的氣象預(yù)報(bào)系統(tǒng),采集的數(shù)據(jù)會含有誤差。因此本文建立了混沌時(shí)間序列支持向量機(jī)短期風(fēng)電功率預(yù)測模型,通過遺傳算法對該預(yù)測模型的參數(shù)進(jìn)行組合優(yōu)化。仿真結(jié)果顯示混沌支持向量機(jī)預(yù)測模型顯示了良好的預(yù)測性能。通過與支持向量機(jī)模型的對比,混沌時(shí)間序列可以包含所有影響因素所攜帶的統(tǒng)計(jì)規(guī)律,混沌支持向量機(jī)預(yù)測模型能有效的降低外在隨機(jī)性因素對風(fēng)電功率預(yù)測精度的影響。
[Abstract]:With the rapid development of the world economy and society, the traditional energy structure has not been able to meet the energy needs of people, people's eyes have shifted from the traditional fossil energy to renewable clean energy.Wind power is the most developed of all renewable energy sources.With the rapid growth of wind power installed capacity at home and abroad, wind power industry is facing problems such as wind power grid connection, fan maintenance and improving wind energy utilization ratio. People pay more and more attention to the rational, safe and effective utilization of wind power.The short-term prediction of wind power can effectively solve these problems.In this paper, the current situation of wind power generation at home and abroad, the research status of wind power forecasting technology, the basic principle and forecasting method, and the developed and running wind power forecasting system at home and abroad are summarized.The errors of short-term wind power prediction are mainly caused by internal and external randomness factors.The intrinsic random factors refer to the defects or imperfections of the prediction system itself, while the external random factors refer to the imperfections of the input data or the errors of the input data itself.In this paper, the influence of randomness factors on short-term wind power prediction is systematically and comprehensively analyzed, and the error caused by randomness factors in short-term wind power prediction is solved by improving and designing a new forecasting system.In order to solve the problem of inherent randomness, this paper establishes the BP neural network short-term wind power prediction model, discusses the determination of parameters and the selection of hidden layers in the BP modeling process, and finally obtains the optimal BP neural network prediction model.The simulation results show that the prediction accuracy and stability of BP model are poor.Aiming at the problem that BP neural network is prone to fall into local minima and has poor stability, a GASABP short-term wind power prediction model based on genetic algorithm (GA) and simulated annealing algorithm (SA) is established.The simulation results show that the prediction accuracy and stability of the prediction system are improved obviously, and the problem of local extremum is solved effectively.Aiming at the limitation of traditional machine learning theory, a support vector machine (SVM) prediction model based on statistical learning theory is established. The cross-validated grid search method is used to select the parameters of the prediction model.Simulation results show that the prediction accuracy of SVM model is obviously higher than that of GASABP model.The comparison of the three models shows that through the continuous optimization of the prediction model, it can effectively reduce the influence of the inherent random factors on the wind power prediction accuracy in the process of wind power prediction.In order to solve the problem of external random factors, the above prediction models have no unified guiding principle in determining the influencing factors of wind power, which need to be determined artificially according to experience, which leads to the imperfection of the determinant factors.In addition, because the domestic wind farm does not set up perfect and accurate weather forecast system, the collected data will contain errors.In this paper, the short-term wind power prediction model of chaotic time series support vector machine is established, and the parameters of the prediction model are optimized by genetic algorithm.Simulation results show that the chaotic support vector machine model shows good prediction performance.Compared with the support vector machine (SVM) model, the chaotic time series can contain the statistical laws carried by all the influencing factors, and the chaotic SVM prediction model can effectively reduce the influence of external random factors on the prediction accuracy of wind power.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614

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本文編號：1770941