建筑节能
建築節能
건축절능
Building Energy EFFICIENCY
2015年
11期
109-112
,共4页
RBF神经网络%微粒群算法%能耗预测模型
RBF神經網絡%微粒群算法%能耗預測模型
RBF신경망락%미립군산법%능모예측모형
radial basis function(RBF) neural network%particle swarm optimization algorithm%pre-diction model of energy consumption
通过研究分析夏热冬冷地区公共建筑能耗变化特点, 建立了RBF神经网络建筑能耗预测模型. 在此基础上运用微粒群算法对模型优化,建立了基于PSO-RBF的建筑能耗预测模型. 利用大量数据构造样本集,运用软件分别对优化前后的预测模型进行训练,并运用到典型公共建筑能耗值的预测实例中. 结果表明基于PSO-RBF的建筑能耗预测模型的学习能力和预测能力强,能较准确地实现公共建筑能耗预测.
通過研究分析夏熱鼕冷地區公共建築能耗變化特點, 建立瞭RBF神經網絡建築能耗預測模型. 在此基礎上運用微粒群算法對模型優化,建立瞭基于PSO-RBF的建築能耗預測模型. 利用大量數據構造樣本集,運用軟件分彆對優化前後的預測模型進行訓練,併運用到典型公共建築能耗值的預測實例中. 結果錶明基于PSO-RBF的建築能耗預測模型的學習能力和預測能力彊,能較準確地實現公共建築能耗預測.
통과연구분석하열동랭지구공공건축능모변화특점, 건립료RBF신경망락건축능모예측모형. 재차기출상운용미립군산법대모형우화,건립료기우PSO-RBF적건축능모예측모형. 이용대량수거구조양본집,운용연건분별대우화전후적예측모형진행훈련,병운용도전형공공건축능모치적예측실례중. 결과표명기우PSO-RBF적건축능모예측모형적학습능력화예측능력강,능교준학지실현공공건축능모예측.
Themodelofenergyconsumptionpredictionisbuiltafteranalyzingcharacteristicson energy consumption changes of public building in hot summer and cold winter area. Particle swarm optimization algorithm is used to optimize the model, and the PSO-RBF neural network prediction model is established. Using the energy consumption data of subject research, the samples of building energy consumption is built. Then the RBF neural network and PSO-RBF neural network are trained on MATLAB. Experiments are conducted to predict energy consumption values of typical public buildings. The results show that accuracy of the prediction model is improved obviously after being optimized, and it has strong learning and predicting ability. The model can predict energy consumption value of public buildings accurately.
