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<p>The adoption of photovoltaic solar panels (PVSP) in rural areas remains uneven despite sustained policy support, indicating the need for predictive frameworks that can reliably inform decentralized energy planning in geographically complex regions. This study develops an interpretable and robust machine learning framework for predicting rural household willingness to adopt PVSP. This paper proposes an explainable, gray level co-occurrence matrix (GLCM)-optimized nested stacking ensemble model applied to survey data from rural households in Jammu and Kashmir. Texture-based GLCM descriptors are used to optimize socio-economic variables by capturing latent inter-feature dependencies, while a multi-level nested stacking technique integrates heterogeneous base learners to enhance generalization and prevent information leakage. The concept of model-agnostic explainability was used by employing the aggregation of global local interpretable model-agnostic explanations (LIME) interpretation of adoption factors among multiple households. From the experimental results, it is clear that good generalization has been achieved with an accuracy of 91.38%, F1 score of 91.87%, and ROC-AUC of 97.05%. These results show statistical significance compared with baselines using McNemar’s test (<i>p</i> &lt; 0.05). Factors related to existing installation of PVSP, promotional marketing awareness drives, awareness about government schemes, climatic conditions, and perception were found to be the most important and stable drivers of adoption willingness from the explainability analysis conducted. The proposed framework offers a relevant tool for identifying adoption barriers and designing targeted interventions, contributing to evidence-based planning for decentralized solar energy transitions in rural and geographically constrained regions.</p>

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Explainable GLCM-optimized nested stacking ensemble model for predicting rural household willingness to use photovoltaic solar panels in Jammu and Kashmir

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Sourabh Shastri,
  • Sachin Kumar,
  • Saloni Singh,
  • Sunny Sharma,
  • Zahin Ansari,
  • Asif Akhtar,
  • Vibhakar Mansotra

摘要

The adoption of photovoltaic solar panels (PVSP) in rural areas remains uneven despite sustained policy support, indicating the need for predictive frameworks that can reliably inform decentralized energy planning in geographically complex regions. This study develops an interpretable and robust machine learning framework for predicting rural household willingness to adopt PVSP. This paper proposes an explainable, gray level co-occurrence matrix (GLCM)-optimized nested stacking ensemble model applied to survey data from rural households in Jammu and Kashmir. Texture-based GLCM descriptors are used to optimize socio-economic variables by capturing latent inter-feature dependencies, while a multi-level nested stacking technique integrates heterogeneous base learners to enhance generalization and prevent information leakage. The concept of model-agnostic explainability was used by employing the aggregation of global local interpretable model-agnostic explanations (LIME) interpretation of adoption factors among multiple households. From the experimental results, it is clear that good generalization has been achieved with an accuracy of 91.38%, F1 score of 91.87%, and ROC-AUC of 97.05%. These results show statistical significance compared with baselines using McNemar’s test (p < 0.05). Factors related to existing installation of PVSP, promotional marketing awareness drives, awareness about government schemes, climatic conditions, and perception were found to be the most important and stable drivers of adoption willingness from the explainability analysis conducted. The proposed framework offers a relevant tool for identifying adoption barriers and designing targeted interventions, contributing to evidence-based planning for decentralized solar energy transitions in rural and geographically constrained regions.