Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546823000757 |
| _version_ | 1797660051182190592 |
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| author | Utsav Bhattarai Tek Maraseni Laxmi Prasad Devkota Armando Apan |
| author_facet | Utsav Bhattarai Tek Maraseni Laxmi Prasad Devkota Armando Apan |
| author_sort | Utsav Bhattarai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:25:10Z |
| format | Article |
| id | doaj.art-88f149b1998549c9bc45211458c6925e |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-03-11T18:25:10Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-88f149b1998549c9bc45211458c6925e2023-10-14T04:45:35ZengElsevierEnergy and AI2666-54682023-10-0114100303Application of machine learning to assess people's perception of household energy in the developing world: A case of NepalUtsav Bhattarai0Tek Maraseni1Laxmi Prasad Devkota2Armando Apan3Institute for Life Sciences and the Environment (ILSE), University of Southern Queensland, Toowoomba, Queensland 4350, Australia; Water Modeling Solutions Pvt. Ltd. (WMS), Kathmandu, Nepal; Corresponding author.Institute for Life Sciences and the Environment (ILSE), University of Southern Queensland, Toowoomba, Queensland 4350, Australia; Centre for Sustainable Agricultural Systems (CSAS), University of Southern Queensland, Toowoomba, Queensland 4350, AustraliaWater Modeling Solutions Pvt. Ltd. (WMS), Kathmandu, Nepal; Nepal Academy of Science and Technology (NAST), Kathmandu, NepalInstitute for Life Sciences and the Environment (ILSE), University of Southern Queensland, Toowoomba, Queensland 4350, Australia; School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, Queensland 4350, Australia; Institute of Environmental Science and Meteorology, University of the Philippines Diliman, Quezon City 1101, PhilippinesResearch 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.http://www.sciencedirect.com/science/article/pii/S2666546823000757EnergyMachine learningPeople's perceptionSocio-economyHouseholdsNepal |
| spellingShingle | Utsav Bhattarai Tek Maraseni Laxmi Prasad Devkota Armando Apan Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal Energy and AI Energy Machine learning People's perception Socio-economy Households Nepal |
| title | Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal |
| title_full | Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal |
| title_fullStr | Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal |
| title_full_unstemmed | Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal |
| title_short | Application of machine learning to assess people's perception of household energy in the developing world: A case of Nepal |
| title_sort | application of machine learning to assess people s perception of household energy in the developing world a case of nepal |
| topic | Energy Machine learning People's perception Socio-economy Households Nepal |
| url | http://www.sciencedirect.com/science/article/pii/S2666546823000757 |
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