摘要
为改进以往神经网络对建筑能耗预测的不足,提出应用遗传算法结合Levenberg-Marquardt算法(GALM)改进神经网络对建筑能耗进行预测。首先,利用遗传算法优化神经网络的权值和阈值;其次,利用Levenberg-Marquardt算法优化神经网络训练,针对影响建筑能耗的主要因素建立GALM神经网络的建筑能耗预测模型。通过建立建筑能耗监测平台采集某公共建筑1个月的能耗数据,对该模型进行训练和测试。实验结果表明,该模型可以准确且高效地对建筑能耗进行短期预测。
In order to improve the conventional method of predicting building energy cortsumptton using ANN, it proposed that the improved neural network optimized by genetic algorithm and Levenberg- Marquardt algorithm, which was applied to predict building energy consumption. First, the genetic algorithm was used to optimize the weight and threshold of ANN, and Levenberg-Marquardt algorithm was adopted to optimize the neural network training. Then, the predicting model based on GALM was set up in terms of the main factors affecting the energy consumption. Furthermore, a public building power consumption data for one month is collected by establishing a monitoring platform to train and test the model. Eventually, the result proves that the model is qualified to predict short-term energy consumption accurately and efficiently.
出处
《建筑节能》
CAS
2015年第9期85-88,共4页
BUILDING ENERGY EFFICIENCY
关键词
神经网络
建筑能耗
短期预测
遗传算法
LM算法
neural network
building energy consumption
short-term prediction
genetic algorithm
Levenberg-Marquardt algorithm
