2022
DOI: 10.1016/j.apenergy.2021.118251
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Exploring household emission patterns and driving factors in Japan using machine learning methods

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Cited by 37 publications
(8 citation statements)
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“…XGBoost also outperformed all eight other models including SVR when predicting domestic space cooling in [60]. XGBoost also performed best against five other models in a study exploring household CO2 emission patterns and the underlying drivers [61]. Less conclusively, depending on the metric used, XGBoost performed as well or better than the top 3-performing algorithms of seven developed to predict district heating load (showing similar performance to SVR and long short-term memory networks) [62].…”
Section: Extreme Gradient Boostingmentioning
confidence: 85%
“…XGBoost also outperformed all eight other models including SVR when predicting domestic space cooling in [60]. XGBoost also performed best against five other models in a study exploring household CO2 emission patterns and the underlying drivers [61]. Less conclusively, depending on the metric used, XGBoost performed as well or better than the top 3-performing algorithms of seven developed to predict district heating load (showing similar performance to SVR and long short-term memory networks) [62].…”
Section: Extreme Gradient Boostingmentioning
confidence: 85%
“…Two smart rooms were prepared in Tokyo, Japan. The room size, monthly rent, and facilities were standard for Tokyo [ 2 , 3 ]. The overviews of the two smart rooms are shown in Figs.…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…Before model construction, LASSO regression was applied for preliminary screening of operational conditions except the technical process. According to current research, 36 the variables could only be stored with a nonzero LASSO coefficient.…”
Section: Data Collection and Formation 211 Multisourcementioning
confidence: 99%