110
Z. Liu et al.
7. Li Y, Han M, Jiang LW (2012) Blowing oxygen volume calculation model of BOF steelmaking
based on oxygen decarburization efficiency prediction. J Dalian Univ Technol. 52(5):725–729
8. Li Y, Han M, Jiang LW (2012) Prediction model of oxygen decarburization efficiency based
on mutual Information case-based reasoning. Inf Control 41(2):261–266
9. Zhang ZY, Sun YG (2018) Prediction of oxygen amount in converter based on grey Elman
neural network. Comput Appl Softw 35(11):109–113
10. Mosavi A, Salimi M, Faizollahzadeh Ardabili S (2019) State of the art of machine learning
models in energy systems, a systematic review. Energies 12(7):1301–1342
11. Deb C, Zhang F, Yang J (2017) A review on time series forecasting techniques for building
energy consumption. Renew Sust Energy Rev 74:902–924
12. Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption
prediction studies. Renew Sust Energy Rev 81:1192–1205
13. Yeh CH (1991) Classification and regression trees (CART). Chemometr Intell Lab 12(3):95–96
14. Bühlmann P, Rütimann P, van de Geer S (2013) Correlated variables in regression: Clustering
and sparse estimation. J Stat Plan Infer 143(11):1835–1858
15. Genuer R, Poggi J-M, Tuleau-Malot C (2010) Variable selection using random forests. Pattern
Recogn Lett 31(14):2225–2236
16. Gregorutti B, Michel B, Saint-Pierre P (2016) Correlation and variable importance in random
forests. Stat Comput 27(3):659–678
Z. Liu et al.
7. Li Y, Han M, Jiang LW (2012) Blowing oxygen volume calculation model of BOF steelmaking
based on oxygen decarburization efficiency prediction. J Dalian Univ Technol. 52(5):725–729
8. Li Y, Han M, Jiang LW (2012) Prediction model of oxygen decarburization efficiency based
on mutual Information case-based reasoning. Inf Control 41(2):261–266
9. Zhang ZY, Sun YG (2018) Prediction of oxygen amount in converter based on grey Elman
neural network. Comput Appl Softw 35(11):109–113
10. Mosavi A, Salimi M, Faizollahzadeh Ardabili S (2019) State of the art of machine learning
models in energy systems, a systematic review. Energies 12(7):1301–1342
11. Deb C, Zhang F, Yang J (2017) A review on time series forecasting techniques for building
energy consumption. Renew Sust Energy Rev 74:902–924
12. Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption
prediction studies. Renew Sust Energy Rev 81:1192–1205
13. Yeh CH (1991) Classification and regression trees (CART). Chemometr Intell Lab 12(3):95–96
14. Bühlmann P, Rütimann P, van de Geer S (2013) Correlated variables in regression: Clustering
and sparse estimation. J Stat Plan Infer 143(11):1835–1858
15. Genuer R, Poggi J-M, Tuleau-Malot C (2010) Variable selection using random forests. Pattern
Recogn Lett 31(14):2225–2236
16. Gregorutti B, Michel B, Saint-Pierre P (2016) Correlation and variable importance in random
forests. Stat Comput 27(3):659–678