本文選題：電力系統(tǒng)負(fù)荷預(yù)測 + 支持向量機(jī)　； 參考：《西安理工大學(xué)》2017年碩士論文

【摘要】：在電力體制改革的新形勢下,電價(jià)競爭機(jī)制介入電力市場,市場參與和系統(tǒng)運(yùn)營人員對短期負(fù)荷預(yù)測越來越看重�？焖贉�(zhǔn)確的負(fù)荷預(yù)測有利于進(jìn)行適當(dāng)?shù)挠?jì)劃電力交易、制定適當(dāng)?shù)牟僮饔?jì)劃和投標(biāo)策略。隨著新理論和新技術(shù)的發(fā)展,對負(fù)荷預(yù)測新方法的研究仍在不斷地深入進(jìn)行。支持向量機(jī)是一種數(shù)據(jù)預(yù)測的常用方法,與其它方法相比,表現(xiàn)出了較好的性能,能較好的實(shí)現(xiàn)結(jié)構(gòu)風(fēng)險(xiǎn)最小化思想,可應(yīng)用于模式識別和處理回歸問題等諸多領(lǐng)域。論文利用支持向量機(jī)在非線性學(xué)習(xí)和預(yù)測性能上的優(yōu)點(diǎn),針對短期電力負(fù)荷預(yù)測的各種影響因素的非線性特性,支持向量機(jī)在參數(shù)選擇上比較困難且傳統(tǒng)方法存在一些缺陷問題;以及不同的核函數(shù)性能一般不同,在支持向量機(jī)的核函數(shù)選擇上仍存在的一些問題,提出基于支持向量機(jī)的電力系統(tǒng)短期負(fù)荷預(yù)測的優(yōu)化方法。分別針對雙線性搜索法、網(wǎng)格搜索法的存在問題,提出一種改進(jìn)的雙線性搜索法和改進(jìn)網(wǎng)格搜索法,通過實(shí)驗(yàn)對比,驗(yàn)證了改進(jìn)方法的預(yù)測精度和時(shí)效性。此外,針對混合核函數(shù)權(quán)重系數(shù)難以準(zhǔn)確選取的問題,采用粒子群優(yōu)化實(shí)現(xiàn)權(quán)重的優(yōu)化求解;針對粒子群算法存在的容易陷入局部最優(yōu)的問題,在迭代過程中,根據(jù)適應(yīng)度值動(dòng)態(tài)調(diào)整慣性權(quán)重、局部加速常數(shù)及全局加速常數(shù)變異粒子,提出了一種改進(jìn)的粒子群算法,實(shí)驗(yàn)結(jié)果表明改進(jìn)粒子群混合核函數(shù)比單核核函數(shù)有更高的預(yù)測精度。
[Abstract]:In the new situation of electric power system reform, the electricity price competition mechanism is involved in the electricity market, market participation and system operators pay more and more attention to short-term load forecasting. Fast and accurate load forecasting is helpful for proper planned electricity transaction, proper operation plan and bidding strategy. With the development of new theory and new technology, the research of new load forecasting methods is still going on. Support vector machine (SVM) is a common method for data prediction. Compared with other methods, it has better performance and can realize structural risk minimization. It can be used in many fields such as pattern recognition and regression problems. Based on the advantages of support vector machine (SVM) in nonlinear learning and forecasting performance, this paper aims at the nonlinear characteristics of various factors affecting short-term power load forecasting. Support vector machine (SVM) is difficult to select parameters and has some defects in traditional methods. An optimization method for short term load forecasting of power system based on support vector machine (SVM) is proposed. In order to solve the problems of bilinear search and mesh search, an improved bilinear search method and an improved grid search method are proposed. The prediction accuracy and timeliness of the improved method are verified by experimental comparison. In addition, to solve the problem that the weight coefficient of hybrid kernel function is difficult to select accurately, particle swarm optimization is used to solve the weight optimization. Based on the dynamic adjustment of inertia weight, local acceleration constant and global acceleration constant, an improved particle swarm optimization algorithm is proposed. The experimental results show that the improved particle swarm hybrid kernel function has higher prediction accuracy than the single kernel function.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM715

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本文編號：2045017