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

2017 International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Song, Chen^1,2,3 ; Zhong-Cheng, Wu^1 ; Hong, Lv^3

High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei

230031, China^1

University of Science and Technology of China, Hefei

230026, China^2

College of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei

230601, China^3

关键词: BP neural networks;    Building energy;    Local minimum point;    Mind evolutionary algorithms;    Mind evolutionary computation;    Optimal networks;    Predictive values;    Time series prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/322/6/062006/pdf
DOI  :  10.1088/1757-899X/322/6/062006

学科分类：材料科学（综合）

来源: IOP

【 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.