自適應加權最小二乘支持向量機的空調負荷預測
發(fā)布時間：2018-10-20 10:45
【摘要】：為了提高建筑空調負荷的預測精度,在分析空調負荷主要影響因素的基礎上提出了一種基于自適應加權最小二乘支持向量機(AWLS-SVM)的建筑空調負荷預測方法。該方法根據(jù)預測誤差的統(tǒng)計特性,采用基于改進正態(tài)分布加權規(guī)則,自適應地賦予每個建模樣本不同的權值,以克服異常樣本點對模型性能的影響。建模過程中采用粒子群優(yōu)化(PSO)算法對模型參數(shù)進行優(yōu)化,以進一步提高模型預測精度�；贒eST模擬數(shù)據(jù)將AWLS-SVM方法應用于南方地區(qū)某辦公建筑的逐時空調負荷預測中,并與徑向基神經(jīng)網(wǎng)絡(RBFNN)模型、LS-SVM模型及WLS-SVM模型作比較,其平均預測絕對誤差分別降低了51.84%、13.95%和3.24%,并進一步基于實際空調負荷數(shù)據(jù)將該方法應用于另一辦公建筑的逐日空調負荷預測中。預測結果表明:AWLS-SVM預測的累積負荷誤差為4.56MW,亦優(yōu)于其他3類模型,證明了AWLS-SVM具有較高的預測精度和較好的泛化能力,是建筑空調負荷預測的一種有效方法。
[Abstract]:In order to improve the precision of building air conditioning load prediction, an adaptive weighted least square support vector machine (AWLS-SVM) based building air conditioning load forecasting method is proposed based on the analysis of the main factors affecting the air conditioning load. According to the statistical characteristics of prediction error, the method adaptively assigns different weights to each modeling sample based on the improved normal distribution weighting rule, in order to overcome the influence of abnormal sample points on the performance of the model. In the process of modeling, particle swarm optimization (PSO) algorithm is used to optimize the model parameters to further improve the prediction accuracy of the model. Based on the DeST simulation data, the AWLS-SVM method is applied to the hourly air conditioning load forecasting of an office building in southern China. The results are compared with the radial basis function neural network (RBF) (RBFNN) model, LS-SVM model and WLS-SVM model. The average absolute error of prediction is reduced by 51.84% and 3.24%, respectively. The method is further applied to the daily air conditioning load forecasting of another office building based on the actual air conditioning load data. The prediction results show that the cumulative load error of AWLS-SVM is 4.56MW, which is better than other three models. It is proved that AWLS-SVM has higher prediction accuracy and better generalization ability, and it is an effective method for building air conditioning load forecasting.
【作者單位】： 福州大學石油化工學院;
【基金】：國家自然科學基金資助項目(6080402,61374133) 高校博士點專項科研基金(20133314120004)~~
【分類號】：TU831.1;TP18
[Abstract]:In order to improve the precision of building air conditioning load prediction, an adaptive weighted least square support vector machine (AWLS-SVM) based building air conditioning load forecasting method is proposed based on the analysis of the main factors affecting the air conditioning load. According to the statistical characteristics of prediction error, the method adaptively assigns different weights to each modeling sample based on the improved normal distribution weighting rule, in order to overcome the influence of abnormal sample points on the performance of the model. In the process of modeling, particle swarm optimization (PSO) algorithm is used to optimize the model parameters to further improve the prediction accuracy of the model. Based on the DeST simulation data, the AWLS-SVM method is applied to the hourly air conditioning load forecasting of an office building in southern China. The results are compared with the radial basis function neural network (RBF) (RBFNN) model, LS-SVM model and WLS-SVM model. The average absolute error of prediction is reduced by 51.84% and 3.24%, respectively. The method is further applied to the daily air conditioning load forecasting of another office building based on the actual air conditioning load data. The prediction results show that the cumulative load error of AWLS-SVM is 4.56MW, which is better than other three models. It is proved that AWLS-SVM has higher prediction accuracy and better generalization ability, and it is an effective method for building air conditioning load forecasting.
【作者單位】： 福州大學石油化工學院;
【基金】：國家自然科學基金資助項目(6080402,61374133) 高校博士點專項科研基金(20133314120004)~~
【分類號】：TU831.1;TP18
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