本文選題：BP神經(jīng)網(wǎng)絡(luò) + 短期電力負(fù)荷　； 參考：《貴州師范大學(xué)》2014年碩士論文

【摘要】：隨著電力市場的不斷發(fā)展，電力負(fù)荷預(yù)測工作成為電力系統(tǒng)管理部門的一項(xiàng)重要工作。準(zhǔn)確地進(jìn)行電力負(fù)荷預(yù)測可以更好地制定電網(wǎng)規(guī)劃方案以及發(fā)電機(jī)組的檢修計(jì)劃，可以更加合理地安排電網(wǎng)的運(yùn)行方式。對(duì)于提高電力企業(yè)的經(jīng)濟(jì)效益和社會(huì)效益、保持電力系統(tǒng)的安全穩(wěn)定運(yùn)行、保障人們?nèi)粘Ｉ畹挠行蜻M(jìn)行具有重要的意義。 本文首先介紹了電力負(fù)荷預(yù)測的研究背景、國內(nèi)外研究現(xiàn)狀以及研究意義，并且敘述了電力負(fù)荷預(yù)測的基本理論；其次，對(duì)現(xiàn)代預(yù)測關(guān)鍵技術(shù)進(jìn)行了詳細(xì)的介紹，，介紹了人工神經(jīng)網(wǎng)絡(luò)的基本理論，研究了BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)、BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法步驟及其優(yōu)缺點(diǎn)，分析了遺傳算法和粒子群優(yōu)化算法的特點(diǎn)以及基本原理；再次，對(duì)BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型進(jìn)行了設(shè)計(jì)，主要是輸入層和輸出層的設(shè)計(jì)、隱含層的設(shè)計(jì)以及轉(zhuǎn)移函數(shù)的確定；最后，分別采用遺傳算法(GA)、粒子群優(yōu)化算法(PSO)以及本文所提出的GA-PSO算法對(duì)BP神經(jīng)網(wǎng)絡(luò)的權(quán)值和閾值進(jìn)行優(yōu)化，分別建立了GA-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型、PSO-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型以及GA-PSO-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型。選取歐洲某地區(qū)的歷史負(fù)荷數(shù)據(jù)、歷史氣溫和日期類型等數(shù)據(jù)進(jìn)行仿真實(shí)驗(yàn)，對(duì)該地區(qū)某一天24小時(shí)各整點(diǎn)時(shí)刻的負(fù)荷進(jìn)行預(yù)測。并分析預(yù)測結(jié)果，比較各預(yù)測模型的性能。仿真實(shí)驗(yàn)結(jié)果表明GA-PSO-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型不僅加快了神經(jīng)網(wǎng)絡(luò)的收斂速度，而且提高了短期電力負(fù)荷的預(yù)測精度。
[Abstract]:With the development of power market, power load forecasting has become an important task in power system management department. Accurate load forecasting can better formulate the power network planning plan and maintenance plan of the generator set, and it can more reasonably arrange the operation mode of the power network. It is of great significance to improve the economic and social benefits of electric power enterprises, to maintain the safe and stable operation of power system and to ensure the orderly operation of people's daily life. This paper first introduces the research background of power load forecasting, the research status and significance at home and abroad, and describes the basic theory of power load forecasting. Secondly, the key technologies of modern forecasting are introduced in detail. This paper introduces the basic theory of artificial neural network, studies the learning algorithm steps of BP neural network and its advantages and disadvantages, analyzes the characteristics and basic principles of genetic algorithm and particle swarm optimization algorithm. The prediction model of BP neural network is designed, including the design of input layer and output layer, the design of hidden layer and the determination of transfer function. The weight and threshold of BP neural network are optimized by genetic algorithm (GA), particle swarm optimization (PSO) and GA-PSO algorithm proposed in this paper. The GA-BP neural network prediction model and the GA-PSO-BP neural network prediction model are established respectively. The data of historical load, historical temperature and date type of a certain region in Europe are selected to carry out simulation experiments to predict the load at each hour of 24 hours a day in that region. The prediction results are analyzed and the performance of each prediction model is compared. The simulation results show that the GA-PSO-BP neural network model not only accelerates the convergence rate of the neural network, but also improves the accuracy of short-term power load prediction.
【學(xué)位授予單位】：貴州師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM715

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