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《Building Energy Efficiency》 2015-09
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Short-term Prediction of Building Energy Consumption Based on GALM Neural Network

LU Kuo;ZHONG Bo-cheng;LYU Ding-hao;LUO Jing;College of Electronic and Electrical Engineering, Shanghai University of Engineering Science;  
In order to improve the conventional method of predicting building energy consumption using ANN, it proposed that the improved neural network optimized by genetic algorithm and LevenbergMarquardt 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.
【CateGory Index】: TU111.195;TP183
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