Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

被引：0
|
作者：
Chen Song [1 ,2 ,3 ]
Wu Zhong-Cheng [1 ]
Lv Hong [3 ]
机构：
[1] Chinese Acad Sci, High Field Magnet Lab, Hefei 230031, Peoples R China
[2] Univ Sci & Technol China, Hefei 230026, Peoples R China
[3] Anhui Jianzhu Univ, Coll Mech & Elect Engn, Hefei 230601, Anhui, Peoples R China
来源：
2017 INTERNATIONAL SYMPOSIUM ON APPLICATION OF MATERIALS SCIENCE AND ENERGY MATERIALS (SAMSE 2017) | 2018年 / 322卷
基金：
中国国家自然科学基金;
关键词：
SYSTEM;
D O I：
10.1088/1757-899X/322/6/062006
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.
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收藏
页数：7
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