本文選題：負荷預測 + 云計算��； 參考：《蘭州理工大學》2017年碩士論文

【摘要】：近年來,隨著計算機技術普遍應用,智能電網迅速發(fā)展。電力部門工作人員為保證系統(tǒng)安全、經濟的運行,對短期負荷預測結果的穩(wěn)定性、準確性、高效性提出了更高的要求。目前短期負荷預測的研究方向主要集中在對預測模型的整體優(yōu)化上,這在一定程度上提高了負荷預測的工作速度和計算精度。但是這些預測方法大多建立在對影響因素的整體分析之上,對各個因素的自身特性考慮的不夠全面,導致預測模型的準確性難以進一步提高,通用性較差。本文根據各個影響因素相關性的不同,對具有更高預測效率的組合預測模型進行了相關研究。首先,本文分析了短期負荷預測的實際應用背景,對當前該領域內的國內外研究現(xiàn)狀進行了歸納總結,在充分對比多種傳統(tǒng)智能預測算法的優(yōu)缺點之后,對未來短期負荷預測的研究重點進行分析。本次選用浙江省某地區(qū)的歷史數據作為訓練樣本和預測樣本,對預測地區(qū)的負荷特性、經濟特性、氣象因素等進行了深入分析,針對原始數據自身存在的不足,對其進行數據預處理,采用雙向比較法篩選修復問題數據,增強了預測結果的可靠性和準確性。其次,為了對預測過程進行精細化研究,本文對影響負荷大小的各個因素進行了確定性相關的分類,利用細菌覓食算法優(yōu)化極限學習機預測模型對確定性相關影響因素負荷進行預測,利用云模型優(yōu)化核極限學習機預測模型對非確定性相關影響因素負荷進行預測,通過對兩種預測模型的預測結果加權求和,得到最終的負荷大小。最后,由于該組合預測模型運算復雜,大大增加了運算的難度,為了解決單機計算資源不足的問題,本文引入云計算對組合預測模型進行并行化改造,提高了預測模型的大數據處理能力,增強了這一新模型的實際應用效果。結果發(fā)現(xiàn),相比于傳統(tǒng)預測方法,本文通過引入云模型優(yōu)化核極限學習機預測模型,增加了對非確定性相關影響因素的考慮范圍,提高了預測結果的準確性,使預測精度提高了0.23%。通過引入云計算,提高了預測模型的并行計算性能,使單次預測時間減少了大約900s,加快了計算的速度,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.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM715

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