Research on social aspects of energy and those applying machine learning(ML)is limited compared to the‘hard’disciplines such as science and engineering.We aim to contribute to this niche through this multidisciplina...Research on social aspects of energy and those applying machine learning(ML)is limited compared to the‘hard’disciplines such as science and engineering.We aim to contribute to this niche through this multidisciplinary study integrating energy,social science and ML.Specifically,we aim:(i)to compare the applicability of different ML models in household(HH)energy;and(ii)to explain people’s perception of HH energy using the most appropriate model.We carried out cross-sectional survey of 323 HHs in a developing country(Nepal)and extracted 14 predictor variables and one response variable.We tested the performance of seven ML models:K-Nearest Neighbors(KNN),Multi-Layer Perceptron(MLP),Extra Trees Classifier(ETC),Random Forest(RF),Ridge Classifier(RC),Multinomial Regression–Logit(MR-L)and Probit(MR-P)in classifying people’s responses.The models were evaluated against six metrics(confusion matrix,precision,f1 score,recall,balanced accuracy and overall accuracy).In this study,ETC outperformed all other models demonstrating a balanced accuracy of 0.79,0.95 and 0.68 respectively for the Agree,Neutral and Disagree response categories.Results showed that,compared to conventional statistical models,data driven ML models are better in classifying people’s perceptions.It was seen that the majority of the surveyed people from rural(68%)and semi-urban areas(67%)tend to resist energy changes due to economic constraints and lack of awareness.Interestingly,most(73%)of the urban residents are open to changes,but still resort to fuel-stacking because of distrust in the state.These grass-root level responses have strong policy implications.展开更多
文摘Research on social aspects of energy and those applying machine learning(ML)is limited compared to the‘hard’disciplines such as science and engineering.We aim to contribute to this niche through this multidisciplinary study integrating energy,social science and ML.Specifically,we aim:(i)to compare the applicability of different ML models in household(HH)energy;and(ii)to explain people’s perception of HH energy using the most appropriate model.We carried out cross-sectional survey of 323 HHs in a developing country(Nepal)and extracted 14 predictor variables and one response variable.We tested the performance of seven ML models:K-Nearest Neighbors(KNN),Multi-Layer Perceptron(MLP),Extra Trees Classifier(ETC),Random Forest(RF),Ridge Classifier(RC),Multinomial Regression–Logit(MR-L)and Probit(MR-P)in classifying people’s responses.The models were evaluated against six metrics(confusion matrix,precision,f1 score,recall,balanced accuracy and overall accuracy).In this study,ETC outperformed all other models demonstrating a balanced accuracy of 0.79,0.95 and 0.68 respectively for the Agree,Neutral and Disagree response categories.Results showed that,compared to conventional statistical models,data driven ML models are better in classifying people’s perceptions.It was seen that the majority of the surveyed people from rural(68%)and semi-urban areas(67%)tend to resist energy changes due to economic constraints and lack of awareness.Interestingly,most(73%)of the urban residents are open to changes,but still resort to fuel-stacking because of distrust in the state.These grass-root level responses have strong policy implications.
