Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal
Article
Bhattarai, Utsav, Maraseni, Tek, Devkota, Laxmi Prasad and Apan, Armando. 2023. "Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal." Energy and AI. 14. https://doi.org/10.1016/j.egyai.2023.100303
| Article Title | Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal |
|---|---|
| ERA Journal ID | 212372 |
| Article Category | Article |
| Authors | Bhattarai, Utsav, Maraseni, Tek, Devkota, Laxmi Prasad and Apan, Armando |
| Journal Title | Energy and AI |
| Journal Citation | 14 |
| Article Number | 100303 |
| Number of Pages | 20 |
| Year | 2023 |
| Publisher | Elsevier BV |
| Place of Publication | Netherlands |
| ISSN | 2666-5468 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.egyai.2023.100303 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2666546823000757 |
| Abstract | 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. © 2023 |
| Keywords | Energy; Machine learning ; People’s perception ; Socio-economy ; Households; Nepal |
| Related Output | |
| Is part of | Integrated climate resilient modelling of renewable energy transition in Nepal |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 410406. Natural resource management |
| Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
| Byline Affiliations | Institute for Life Sciences and the Environment |
| Water Modeling Solutions, Nepal | |
| Centre for Sustainable Agricultural Systems | |
| Nepal Academy of Science and Technology | |
| School of Surveying and Built Environment | |
| University of the Philippines Diliman, Philippines |
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https://research.usq.edu.au/item/z255v/application-of-machine-learning-to-assess-people-s-perception-of-household-energy-in-the-developing-world-a-case-of-nepal
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