建筑节能
建築節能
건축절능
CONSTRUCTION CONSERVES ENERGY
2014年
11期
79-81,100
,共4页
建筑能耗%短期预测%神经网络%BP算法%Levenberg-Marquardt算法
建築能耗%短期預測%神經網絡%BP算法%Levenberg-Marquardt算法
건축능모%단기예측%신경망락%BP산법%Levenberg-Marquardt산법
building energy consumption%short-term prediction%neural network%Back Propagation al-gorithm%Levenberg-Marquardt algorithm
为解决BP神经网络预测速度慢不适于建筑能耗短期预测的问题，采用Levenberg-Marquardt算法改进BP神经网络建立了基于LMBP神经网络的建筑能耗短期预测模型。通过某建筑物1个月的电量，对模型进行训练和测试，结果表明基于LMBP神经网络的预测模型预测速度显著提高，预测精度满足实际需要，适用于建筑能耗短期预测。
為解決BP神經網絡預測速度慢不適于建築能耗短期預測的問題，採用Levenberg-Marquardt算法改進BP神經網絡建立瞭基于LMBP神經網絡的建築能耗短期預測模型。通過某建築物1箇月的電量，對模型進行訓練和測試，結果錶明基于LMBP神經網絡的預測模型預測速度顯著提高，預測精度滿足實際需要，適用于建築能耗短期預測。
위해결BP신경망락예측속도만불괄우건축능모단기예측적문제，채용Levenberg-Marquardt산법개진BP신경망락건립료기우LMBP신경망락적건축능모단기예측모형。통과모건축물1개월적전량，대모형진행훈련화측시，결과표명기우LMBP신경망락적예측모형예측속도현저제고，예측정도만족실제수요，괄용우건축능모단기예측。
BP neural network isn’t suitable for short-term prediction of building energy consumption because of its slow prediction speed. Therefore, Levenberg-Marquardt algorithm is adopted to establish short-term prediction model based on LMBP neural network instead of BP neural network. Then the power load data of a building for one month are used to train and test the improved prediction model. Results show that the efficiency of the advanced model is greatly increased. Besides, the prediction accuracy is in the allowed range. It indicates the prediction model based on LMBP neural network is applicable to short-term prediction of building energy consumption.
