本文選題：短期電力負(fù)荷預(yù)測(cè)　切入點(diǎn)：人工神經(jīng)網(wǎng)絡(luò)　出處：《浙江大學(xué)》2017年碩士論文

【摘要】：短期負(fù)荷預(yù)測(cè)是電力系統(tǒng)運(yùn)行和控制的重要基礎(chǔ)性工作,一直是學(xué)術(shù)研究的熱點(diǎn)問(wèn)題。由于電力負(fù)荷歷史數(shù)據(jù)本質(zhì)上是一個(gè)隨機(jī)非平穩(wěn)序列,完全無(wú)誤差的預(yù)測(cè)目前是不可能的,因此研究者們一直致力于提升預(yù)測(cè)的精度。人工神經(jīng)網(wǎng)絡(luò)具有自學(xué)習(xí)、泛化能力強(qiáng)等優(yōu)點(diǎn),已被廣泛應(yīng)用于短期電力負(fù)荷預(yù)測(cè)中,取得了較為理想的效果。近年來(lái),人工神經(jīng)網(wǎng)絡(luò)領(lǐng)域又取得了可喜的突破,出現(xiàn)了深度學(xué)習(xí)這一新的研究領(lǐng)域。本文基于人工神經(jīng)網(wǎng)絡(luò)的最新發(fā)展成果,結(jié)合實(shí)際數(shù)據(jù)對(duì)短期電力負(fù)荷預(yù)測(cè)問(wèn)題進(jìn)行了相關(guān)研究。主要內(nèi)容包括:(1)建立了基于改進(jìn)粒子群算法優(yōu)化極限學(xué)習(xí)機(jī)的短期負(fù)荷點(diǎn)預(yù)測(cè)模型。該模型將改進(jìn)粒子群算法與極限學(xué)習(xí)機(jī)結(jié)合,利用改進(jìn)粒子群算法強(qiáng)大的全局搜索能力對(duì)極限學(xué)習(xí)機(jī)的輸入權(quán)值及隱含層偏置矩陣進(jìn)行尋優(yōu)�；谟脩魧�(shí)際負(fù)荷數(shù)據(jù)得到的仿真結(jié)果驗(yàn)證了該模型的有效性。(2)基于深度學(xué)習(xí)領(lǐng)域的神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),分別建立了帶詞嵌入層的多層長(zhǎng)短期記憶網(wǎng)絡(luò)短期點(diǎn)預(yù)測(cè)模型和帶詞嵌入層和卷積層的長(zhǎng)短期記憶網(wǎng)絡(luò)點(diǎn)預(yù)測(cè)模型�；谟脩魧�(shí)際負(fù)荷數(shù)據(jù),驗(yàn)證了上述模型的有效性,并與基于改進(jìn)粒子群優(yōu)化極限學(xué)習(xí)機(jī)的模型進(jìn)行了對(duì)比。(3)針對(duì)電力負(fù)荷預(yù)測(cè)中的不確定性,建立了基于改進(jìn)粒子群優(yōu)化極限學(xué)習(xí)機(jī)的短期區(qū)間預(yù)測(cè)模型;同時(shí),提出了一種改進(jìn)的比例系數(shù)法,能夠在點(diǎn)預(yù)測(cè)的基礎(chǔ)上生成更合理的預(yù)測(cè)區(qū)間,基于用戶實(shí)際負(fù)荷數(shù)據(jù)的算例表明,該方法可以得到比較理想的區(qū)間預(yù)測(cè)結(jié)果。
[Abstract]:Short-term load forecasting is an important basic work in the operation and control of power system and has been a hot topic in academic research. Because the historical data of power load is essentially a random non-stationary sequence. At present, it is impossible to predict without error, so researchers have been working to improve the accuracy of prediction. Artificial neural network has been widely used in short-term power load forecasting because of its advantages of self-learning and strong generalization ability. In recent years, the field of artificial neural network has made a gratifying breakthrough, and the new research field of deep learning has emerged. This paper based on the latest development of artificial neural network, Based on the actual data, this paper studies the short-term power load forecasting problem. The main contents include: 1) A short-term load point forecasting model based on improved particle swarm optimization algorithm for extreme learning machine is established. The model will improve particle size. Group algorithm and extreme learning machine, Using the powerful global search ability of improved particle swarm optimization (PSO) algorithm, the input weights and hidden layer bias matrices of LLMs are optimized. The simulation results based on the actual load data of users verify the validity of the model. Neural network structure based on deep learning domain, The short and short term point prediction models with word embedding layer and long and short term memory network with word embedding layer and convolution layer are established respectively. Based on the actual load data of users, the validity of the above model is verified. Compared with the model based on improved particle swarm optimization (PSO) limit learning machine, a short-term interval prediction model based on improved particle swarm optimization (PSO) limit learning machine is established in view of the uncertainty in power load forecasting. An improved proportional coefficient method is proposed, which can generate a more reasonable prediction interval based on the point prediction. An example based on the actual load data shows that the method can get a better interval prediction result.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM715

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本文編號(hào)：1674777