本文選題：公共樓宇空調(diào)系統(tǒng) + 日前負(fù)荷預(yù)測��； 參考：《電網(wǎng)與清潔能源》2016年11期

【摘要】：隨著我國城市建設(shè)的推進(jìn),公共樓宇的用電能耗增長迅速。為加強(qiáng)能耗管理、降低能耗水平,對(duì)公共樓宇空調(diào)系統(tǒng)日前用電負(fù)荷進(jìn)行預(yù)測是工作的基礎(chǔ)。針對(duì)當(dāng)前公共樓宇空調(diào)系統(tǒng)日前負(fù)荷預(yù)測累積誤差大的現(xiàn)象,提出對(duì)日前24 h單獨(dú)建立負(fù)荷預(yù)測模型的并行預(yù)測策略。然后融合主成分分析和模糊C均值聚類對(duì)數(shù)據(jù)進(jìn)行預(yù)處理,形成合適規(guī)模及變量維度的訓(xùn)練數(shù)據(jù),將其作為支持向量機(jī)預(yù)測模型的輸入,并通過粒子群算法對(duì)SVM的模型參數(shù)進(jìn)行自適應(yīng)尋優(yōu)。以實(shí)際公共樓宇空調(diào)負(fù)荷歷史數(shù)據(jù)為基礎(chǔ),對(duì)比分析所提出的算法與串行預(yù)測策略及傳統(tǒng)交叉驗(yàn)證試湊參數(shù)的SVM預(yù)測算法,結(jié)果表明提出的方法充分利用了公共樓宇空調(diào)負(fù)荷的特點(diǎn),預(yù)測精度高、速度快。
[Abstract]:With the development of urban construction in China, the power consumption of public buildings is increasing rapidly. In order to strengthen the energy consumption management and reduce the energy consumption level, it is the basis of the work to forecast the daily load of air conditioning system in public buildings. In view of the large accumulated error of daily load forecasting in the air conditioning system of public buildings, a parallel forecasting strategy is proposed to establish the load forecasting model alone for 24 hours before the day. Then the data are preprocessed by combining principal component analysis and fuzzy C-means clustering to form training data of appropriate scale and variable dimension, which are used as input of support vector machine (SVM) prediction model. Particle swarm optimization (PSO) is used to optimize the model parameters of SVM. Based on the historical data of air conditioning load in real public buildings, the proposed algorithm is compared with the serial prediction strategy and the SVM prediction algorithm based on the traditional cross-validation test parameters. The results show that the proposed method makes full use of the characteristics of air conditioning load in public buildings and has high prediction accuracy and high speed.
【作者單位】： 國網(wǎng)江蘇省電力公司;河海大學(xué)能源與電氣學(xué)院;江蘇省配用電與能效工程技術(shù)研究中心;南京河�？萍加邢薰�;
【基金】：國家自然科學(xué)基金項(xiàng)目(51577051) 國家電網(wǎng)公司科技項(xiàng)目(SGJS0000YXJS1501044)~~
【分類號(hào)】：TU831.2

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