Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal

摘要: 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 …