An improved office building cooling load prediction model based on multivariable linear regression

2015 ◽  
Vol 107 ◽  
pp. 445-455 ◽  
Author(s):  
Guo Qiang ◽  
Tian Zhe ◽  
Ding Yan ◽  
Zhu Neng
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Office Building ◽  
Cooling Load ◽  
Load Prediction ◽  
Model Based ◽  
Building Cooling

Regional Short-Term Earthquake Prediction Model Based on BP Neural Network

2012 ◽  
Vol 166-169 ◽  
pp. 2309-2314
Author(s):  
Wu Sheng Hu ◽  
Hao Wang ◽  
Hong Lin Nie
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Prediction Model ◽  
Earthquake Prediction ◽  
Linear Regression Analysis ◽  
Earthquake Magnitude ◽  
Quantitative Prediction ◽  
Earthquake Parameters ◽  
Advantages And Disadvantages ◽  
Model Based

A method which gives the quantitative prediction for earthquake magnitude is proposed in this paper. By this method, after calculating the earthquake parameters and the astronomical time-varying parameters, an earthquake prediction model can be established to gives the quantitative prediction for earthquake magnitude in the future prediction period. In this research, the research object was the experimental areas, the prediction period was 6 months, and Linear Regression analysis and conventional BP (Back Propagation) Neural Network were used respectively in prediction. Through backtracking test, the RMSEs(root mean square error) of earthquake magnitude prediction are ±0.78 ML and ±0.61 ML. Then after summarizing the advantages and disadvantages of the two methods, an integrated model based on linear regression and neural network was proposed. Through backtracking test, the RMSE of earthquake magnitude prediction reaches ± 0.41 ML, results improving significantly.

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