Skip to main content

Advertisement

Table 4 Linear regression models explaining logaritmized CPD with the branch information and combined models with multiple influencing factor

From: Explaining and predicting annual electricity demand of enterprises – a case study from Switzerland

  Model 5 Model 6 Model 7
(Intercept) 2.65 (0.18) 1.77 (0.46) 1.82 (0.59)
branche C 2.85 (0.35) 3.05 (0.49)  
branche D 1.25 (0.31) 2.42 (0.54)  
branche F 1.24 (0.19) 1.13 (0.32)  
branche G 1.56 (0.21) 1.27 (0.34) 1.26 (0.33)
branche I 2.17 (0.21) 1.94 (0.35) 1.83 (0.34)
branche J 1.08 (0.24) 1.19 (0.37)  
branche K 1.04 (0.23) 1.26 (0.38)  
branche L 1.15 (0.20) 1.46 (0.36)  
branche M 0.65 (0.26) 0.85 (0.44)  
branche O 0.88 (0.21) 0.39 (0.39)  
branche Q 0.90 (0.24) 0.99 (0.36) 0.98 (0.34)
opening hours per week   0.00 (0.00) 0.01 (0.00)
combined number of ratings   0.01 (0.01) 0.01 (0.00)
log(area + 1)   0.13 (0.05) 0.09 (0.08)
R2 0.09 0.15 0.19
Adj. R2 0.08 0.13 0.18
Num. obs. 1810 700 298
RMSE 1.61 1.57 1.51
  1. p<0.001, p<0.01, p<0.05