本文選題：光伏發(fā)電 + 短期預(yù)測��； 參考：《西華大學(xué)》2014年碩士論文

【摘要】：光伏發(fā)電輸出功率的不穩(wěn)定性會對所接入的電網(wǎng)造成沖擊,因此需要對光伏發(fā)電功率進(jìn)行預(yù)測以保證電網(wǎng)的合理調(diào)度。但是光伏發(fā)電功率在天氣、云層、濕度、季節(jié)等多種因素的影響下,表現(xiàn)出十分復(fù)雜的非線性特性,難以精確預(yù)測。而且預(yù)測時段越長,預(yù)測誤差也就越大。由于在光伏發(fā)電中,未來幾個小時內(nèi)的發(fā)電功率對電網(wǎng)調(diào)度具有非常重要和直接的影響,而且其預(yù)測精度一般比長時預(yù)測精度要高,因此,本課題主要針對光伏發(fā)電系統(tǒng)輸出功率短期預(yù)測技術(shù)進(jìn)行了研究。 首先,本論文對目前常用的短期預(yù)測方法進(jìn)行了介紹,分析了不同預(yù)測方法的理論基礎(chǔ)及其特點(diǎn)；從分析中看出,神經(jīng)網(wǎng)絡(luò)預(yù)測技術(shù)作為其中一種具有智能化特點(diǎn)的預(yù)測手段,可以很好的處理非線性問題,能較好的適用于光伏發(fā)電功率的預(yù)測。 然后,本論文介紹了BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)、學(xué)習(xí)規(guī)則和BP神經(jīng)網(wǎng)絡(luò)的建模過程,探討了BP神經(jīng)網(wǎng)絡(luò)在光伏發(fā)電功率預(yù)測中的建模方法；并對兩種典型的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型進(jìn)行了實驗分析。實例一表明,天氣因素對預(yù)測模型精度的提高有著重要影響；實例二中加入了常見的天氣分類這一因素,能在一定程度上提高預(yù)測精度,但是依然不能很好地解決預(yù)測過程中因天氣因素而導(dǎo)致的誤差突然加大,甚至模型失效的不足。 本論文針對傳統(tǒng)的BP神經(jīng)網(wǎng)絡(luò)預(yù)測算法存在天氣分類過于簡單、不能很好地符合待預(yù)測日天氣類型的問題,通過分析光伏發(fā)電的影響因素,基于歷史數(shù)據(jù)提出合理的假設(shè),提出了基于太陽輻射功率曲線匹配的預(yù)測模型。該模型以太陽輻射功率曲線作為匹配標(biāo)準(zhǔn),在歷史數(shù)據(jù)庫中查找與預(yù)測日太陽輻射曲線匹配的歷史數(shù)據(jù),然后利用匹配得到的相似日、相似時段的歷史數(shù)據(jù)構(gòu)建并訓(xùn)練神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測,實驗表明,該模型可以達(dá)到較好的預(yù)測效果。 最后,將本論文所提出的預(yù)測模型與傳統(tǒng)預(yù)測模型進(jìn)行對比表明,本論文所建立的模型具有較好的短期預(yù)測能力,能達(dá)到較高的預(yù)測精度,對光伏發(fā)電短期預(yù)測能起到很好的指導(dǎo)作用。
[Abstract]:The instability of photovoltaic output power will impact the connected power grid, so it is necessary to predict the photovoltaic power generation power to ensure the reasonable dispatch of the grid. However, under the influence of weather, cloud, humidity, season and other factors, photovoltaic power has a very complex nonlinear characteristics, which is difficult to predict accurately. And the longer the prediction period, the greater the prediction error. In photovoltaic power generation, the power generation in the next few hours has a very important and direct impact on the power grid dispatching, and its prediction accuracy is generally higher than that in the long term. This paper mainly focuses on the short-term prediction technology of output power of photovoltaic power generation system. First of all, this paper introduces the current commonly used short-term forecasting methods, analyzes the theoretical basis and characteristics of different forecasting methods, and finds out from the analysis that the neural network prediction technology is one of the intelligent forecasting methods. It can deal with nonlinear problems well and can be applied to the prediction of photovoltaic power generation. Then, this paper introduces the structure of BP neural network, learning rules and BP neural network modeling process, and discusses the modeling method of BP neural network in photovoltaic generation power prediction. Two typical BP neural network prediction models are analyzed experimentally. Example 1 shows that the weather factors have an important effect on the accuracy of the prediction model, and the common weather classification is added to the second example, which can improve the prediction accuracy to a certain extent. However, the error caused by weather factors in the prediction process can not be solved well, even the deficiency of model failure. This paper aims at the problem that the traditional BP neural network forecasting algorithm is too simple to classify weather, which can not accord with the forecast weather type well. By analyzing the influencing factors of photovoltaic power generation, the reasonable assumptions are put forward based on the historical data. A prediction model based on solar radiation power curve matching is proposed. In this model, the solar radiation power curve is used as the matching standard, and the historical data matching with the predicted solar radiation curve is found in the historical database, and then the similar day is obtained by using the matching data. The historical data of similar periods are constructed and trained to predict. The experimental results show that the model can achieve a good prediction effect. Finally, the comparison between the proposed prediction model and the traditional prediction model shows that the model established in this paper has a better short-term forecasting ability and can achieve a higher prediction accuracy. It can play a good guiding role in short-term prediction of photovoltaic power generation.
【學(xué)位授予單位】：西華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM615

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相關(guān)期刊論文 前1條

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本文編號：1891172