本文關(guān)鍵詞：基于模糊神經(jīng)網(wǎng)絡(luò)的供熱負(fù)荷預(yù)測的研究　出處：《青島理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 集中供熱 模糊神經(jīng)網(wǎng)絡(luò) 負(fù)荷預(yù)測 預(yù)測控制

【摘要】：隨著我國經(jīng)濟(jì)的發(fā)展，供熱面積也在不斷地擴(kuò)大，供熱的能耗也不斷地增加。在我國三北地區(qū)，僅供熱能耗這一項就占其總能耗的27.2%。此外，我國的能源的不斷耗竭以及自動控制技術(shù)的不斷發(fā)展，使得在供熱管網(wǎng)方面的投資也逐漸增加，供熱管網(wǎng)研究的重要性也在日益彰顯。由于集中供熱管網(wǎng)系統(tǒng)龐大性以及復(fù)雜性，精確的集中供熱系統(tǒng)熱負(fù)荷預(yù)測值對運(yùn)行效率的提高，以及環(huán)保和節(jié)能等方面都具有非常重要的意義。 本論文首先在對供熱負(fù)荷預(yù)測的特點(diǎn)和現(xiàn)狀總結(jié)的基礎(chǔ)上，分析了可能影響負(fù)荷的主要因素，對影響因素采用模糊量化的方式進(jìn)行研究處理，進(jìn)而提出在供熱負(fù)荷預(yù)測中應(yīng)用模糊神經(jīng)網(wǎng)絡(luò)系統(tǒng)。本系統(tǒng)的設(shè)計核心是BP神經(jīng)網(wǎng)絡(luò)，即將模糊量化后的影響因素作為系統(tǒng)的輸入值，去調(diào)整神經(jīng)網(wǎng)絡(luò)的權(quán)值，從而得到預(yù)測的網(wǎng)絡(luò)模型。 確定預(yù)測模型的運(yùn)行參數(shù)后，運(yùn)用MATLAB7.0進(jìn)行預(yù)測仿真，，仿真結(jié)果表明，預(yù)測的結(jié)果能夠滿足要求，相對誤差在合理的范圍內(nèi)，并且模糊神經(jīng)網(wǎng)絡(luò)算法比單純神經(jīng)網(wǎng)絡(luò)算法具有更好的預(yù)測精度，泛化能力更強(qiáng)，從而能更好的提高了供熱品質(zhì)，節(jié)約能源。 最后，在實現(xiàn)熱負(fù)荷預(yù)測的基礎(chǔ)上，對集中供熱預(yù)測控制進(jìn)行研究。從數(shù)學(xué)模型的角度來看，由于供熱系統(tǒng)的非線性的特點(diǎn)，使得控制復(fù)雜性增加，本文對預(yù)測控制的必要性以及控制策略進(jìn)行了探討。
[Abstract]:With the development of our country ' s economy , the heating area is expanding constantly , and the energy consumption of heat supply is increasing continuously . In our country ' s three northern areas , only one item of heating energy consumption accounts for 27 . 2 % of its total energy consumption . In addition , our country ' s energy consumption and the continuous development of automatic control technology make the investment in the heating pipe network gradually increase . On the basis of summarizing the characteristics and present situation of heating load forecasting , this paper analyzes the main factors which may affect the load , carries out the research and treatment in the way of fuzzy quantification on the influencing factors , and puts forward the application of the fuzzy neural network system in the forecasting of heating load . The design core of the system is the BP neural network , and the influence factors after fuzzy quantization are used as input values of the system to adjust the weights of the neural networks to obtain the predicted network model . The simulation results show that the predicted results can meet the requirement and the relative error is within a reasonable range , and the fuzzy neural network algorithm has better prediction accuracy and generalization ability than the simple neural network algorithm , which can improve the heat supply quality and save energy . Finally , on the basis of realizing the thermal load forecasting , the central heating prediction control is studied . From the point of view of the mathematical model , the control complexity is increased due to the nonlinear characteristics of the heating system , and the necessity and control strategy of the predictive control are discussed .

【學(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP183;TU995

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本文編號：1377135