本文選題：負(fù)荷預(yù)測(cè) + 云計(jì)算��； 參考：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：近年來(lái),隨著計(jì)算機(jī)技術(shù)普遍應(yīng)用,智能電網(wǎng)迅速發(fā)展。電力部門工作人員為保證系統(tǒng)安全、經(jīng)濟(jì)的運(yùn)行,對(duì)短期負(fù)荷預(yù)測(cè)結(jié)果的穩(wěn)定性、準(zhǔn)確性、高效性提出了更高的要求。目前短期負(fù)荷預(yù)測(cè)的研究方向主要集中在對(duì)預(yù)測(cè)模型的整體優(yōu)化上,這在一定程度上提高了負(fù)荷預(yù)測(cè)的工作速度和計(jì)算精度。但是這些預(yù)測(cè)方法大多建立在對(duì)影響因素的整體分析之上,對(duì)各個(gè)因素的自身特性考慮的不夠全面,導(dǎo)致預(yù)測(cè)模型的準(zhǔn)確性難以進(jìn)一步提高,通用性較差。本文根據(jù)各個(gè)影響因素相關(guān)性的不同,對(duì)具有更高預(yù)測(cè)效率的組合預(yù)測(cè)模型進(jìn)行了相關(guān)研究。首先,本文分析了短期負(fù)荷預(yù)測(cè)的實(shí)際應(yīng)用背景,對(duì)當(dāng)前該領(lǐng)域內(nèi)的國(guó)內(nèi)外研究現(xiàn)狀進(jìn)行了歸納總結(jié),在充分對(duì)比多種傳統(tǒng)智能預(yù)測(cè)算法的優(yōu)缺點(diǎn)之后,對(duì)未來(lái)短期負(fù)荷預(yù)測(cè)的研究重點(diǎn)進(jìn)行分析。本次選用浙江省某地區(qū)的歷史數(shù)據(jù)作為訓(xùn)練樣本和預(yù)測(cè)樣本,對(duì)預(yù)測(cè)地區(qū)的負(fù)荷特性、經(jīng)濟(jì)特性、氣象因素等進(jìn)行了深入分析,針對(duì)原始數(shù)據(jù)自身存在的不足,對(duì)其進(jìn)行數(shù)據(jù)預(yù)處理,采用雙向比較法篩選修復(fù)問(wèn)題數(shù)據(jù),增強(qiáng)了預(yù)測(cè)結(jié)果的可靠性和準(zhǔn)確性。其次,為了對(duì)預(yù)測(cè)過(guò)程進(jìn)行精細(xì)化研究,本文對(duì)影響負(fù)荷大小的各個(gè)因素進(jìn)行了確定性相關(guān)的分類,利用細(xì)菌覓食算法優(yōu)化極限學(xué)習(xí)機(jī)預(yù)測(cè)模型對(duì)確定性相關(guān)影響因素負(fù)荷進(jìn)行預(yù)測(cè),利用云模型優(yōu)化核極限學(xué)習(xí)機(jī)預(yù)測(cè)模型對(duì)非確定性相關(guān)影響因素負(fù)荷進(jìn)行預(yù)測(cè),通過(guò)對(duì)兩種預(yù)測(cè)模型的預(yù)測(cè)結(jié)果加權(quán)求和,得到最終的負(fù)荷大小。最后,由于該組合預(yù)測(cè)模型運(yùn)算復(fù)雜,大大增加了運(yùn)算的難度,為了解決單機(jī)計(jì)算資源不足的問(wèn)題,本文引入云計(jì)算對(duì)組合預(yù)測(cè)模型進(jìn)行并行化改造,提高了預(yù)測(cè)模型的大數(shù)據(jù)處理能力,增強(qiáng)了這一新模型的實(shí)際應(yīng)用效果。結(jié)果發(fā)現(xiàn),相比于傳統(tǒng)預(yù)測(cè)方法,本文通過(guò)引入云模型優(yōu)化核極限學(xué)習(xí)機(jī)預(yù)測(cè)模型,增加了對(duì)非確定性相關(guān)影響因素的考慮范圍,提高了預(yù)測(cè)結(jié)果的準(zhǔn)確性,使預(yù)測(cè)精度提高了0.23%。通過(guò)引入云計(jì)算,提高了預(yù)測(cè)模型的并行計(jì)算性能,使單次預(yù)測(cè)時(shí)間減少了大約900s,加快了計(jì)算的速度,提高了工作人員的工作效率。
[Abstract]:In recent years, with the widespread application of computer technology, smart grid has developed rapidly.In order to ensure the safe and economical operation of the system, the power department staff put forward higher requirements for the stability, accuracy and efficiency of the short-term load forecasting results.At present, the research direction of short-term load forecasting is mainly focused on the overall optimization of forecasting model, which improves the working speed and calculation accuracy of load forecasting to a certain extent.However, most of these prediction methods are based on the overall analysis of the influencing factors, and the characteristics of each factor are not fully considered, resulting in the accuracy of the prediction model is difficult to further improve, and the generality is poor.In this paper, a combination forecasting model with higher prediction efficiency is studied according to the different correlation between different factors.First of all, this paper analyzes the practical application background of short-term load forecasting, summarizes the current domestic and foreign research status in this field, after fully comparing the advantages and disadvantages of many traditional intelligent forecasting algorithms.The emphasis of future short-term load forecasting is analyzed.In this paper, the historical data of a certain area of Zhejiang Province are selected as training samples and forecasting samples, and the load characteristics, economic characteristics and meteorological factors of the predicted area are analyzed in depth, aiming at the shortcomings of the original data itself.The data preprocessing and bidirectional comparison method are used to screen the restoration data, which enhances the reliability and accuracy of the prediction results.Secondly, in order to study the forecasting process in detail, this paper classifies the factors that affect the load size by deterministic correlation.The bacterial foraging algorithm was used to optimize the prediction model of deterministic factors, and the cloud model was used to predict the load of non-deterministic factors.The final load size is obtained by weighted summation of the forecasting results of the two forecasting models.Finally, due to the complex operation of the combined prediction model, it greatly increases the difficulty of calculation. In order to solve the problem of insufficient computing resources, this paper introduces cloud computing to transform the composite prediction model into parallel.The ability of big data to deal with the prediction model is improved, and the practical application effect of the new model is enhanced.The results show that compared with the traditional prediction methods, the cloud model is introduced to optimize the prediction model of the kernel limit learning machine, which increases the scope of consideration of the non-deterministic related factors and improves the accuracy of the prediction results.The prediction accuracy is improved by 0.23.By introducing cloud computing, the parallel computing performance of the prediction model is improved, the time of single prediction is reduced about 900s, the speed of calculation is accelerated, and the work efficiency of staff is improved.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM715

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