计算机技术与发展
計算機技術與髮展
계산궤기술여발전
COMPUTER TECHNOLOGY AND DEVELOPMENT
2015年
6期
216-218,223
,共4页
建筑能耗%数据采集%短期预测%神经网络%BP算法%LM算法
建築能耗%數據採集%短期預測%神經網絡%BP算法%LM算法
건축능모%수거채집%단기예측%신경망락%BP산법%LM산법
building energy consumption%data acquisition%short-term prediction%neural network%Back Propagation algorithm%Levenberg-Marquardts algorithm
建筑能耗短期预测对实时性要求较高，传统神经网络存在收敛速度慢的缺点。为此，采用LM算法改进标准BP神经网络，建立了基于LM算法的建筑能耗预测模型。首先通过理论说明该算法的先进性，然后设计一套建筑能耗数据采集系统和建立基于LMBP神经网络的建筑能耗预测模型，最后采集某建筑一个月的整点电量作为预测模型的实验数据。实验结果表明，该模型明显提高了训练速度，且预测精度满足实际需求，说明了LMBP神经网络适用于建筑能耗短期预测。
建築能耗短期預測對實時性要求較高，傳統神經網絡存在收斂速度慢的缺點。為此，採用LM算法改進標準BP神經網絡，建立瞭基于LM算法的建築能耗預測模型。首先通過理論說明該算法的先進性，然後設計一套建築能耗數據採集繫統和建立基于LMBP神經網絡的建築能耗預測模型，最後採集某建築一箇月的整點電量作為預測模型的實驗數據。實驗結果錶明，該模型明顯提高瞭訓練速度，且預測精度滿足實際需求，說明瞭LMBP神經網絡適用于建築能耗短期預測。
건축능모단기예측대실시성요구교고，전통신경망락존재수렴속도만적결점。위차，채용LM산법개진표준BP신경망락，건립료기우LM산법적건축능모예측모형。수선통과이론설명해산법적선진성，연후설계일투건축능모수거채집계통화건립기우LMBP신경망락적건축능모예측모형，최후채집모건축일개월적정점전량작위예측모형적실험수거。실험결과표명，해모형명현제고료훈련속도，차예측정도만족실제수구，설명료LMBP신경망락괄용우건축능모단기예측。
The traditional neural network is too slow in term of convergence speed to meet the high real-time requirements of short-term prediction of building energy consumption. Therefore, LM algorithm is adopted instead of conventional BP algorithm to establish the building energy consumption model. Firstly through theoretical description of the advanced algorithm,then design a set of data acquisition system to monitor building energy consumption and set up the prediction model based on LMBP neural network. Finally a building’ s 24-hour power consumption data for one month is collected by the data acquisition system as the experimental samples to verify the model. Empirical results show that the LMBP neural network prediction model significantly improves the training speed,precisely enough to meet the actual demand. Thus,the model is adequate for short-term prediction of building energy consumption.
