A novel application of machine learning techniques for activity-based load disaggregation in rural off-grid, isolated solar systems

Varun Mehra, Rajeev J. Ram, Claudio Vergara · 2016

Summary. This paper develops machine learning methods to disaggregate household electricity demand in rural off-grid solar systems in India. By analyzing power usage data from individual homes, the researchers use classification and clustering algorithms to identify which appliances are running and predict future demand. Understanding activity-based electricity patterns helps rural solar systems right-size batteries and panels, reducing costs while ensuring reliable power access.

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Mehra, V., Ram, R. J., & Vergara, C.. (2016). A novel application of machine learning techniques for activity-based load disaggregation in rural off-grid, isolated solar systems. https://doi.org/10.1109/ghtc.2016.7857308

@article{mehra-2016-novel-application-machine-learning-techniques,
  title   = {A novel application of machine learning techniques for activity-based load disaggregation in rural off-grid, isolated solar systems},
  author  = {Varun Mehra and Rajeev J. Ram and Claudio Vergara},
  year    = {2016},
  doi     = {10.1109/ghtc.2016.7857308},
  url     = {https://doi.org/10.1109/ghtc.2016.7857308}
}

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TI  - A novel application of machine learning techniques for activity-based load disaggregation in rural off-grid, isolated solar systems
AU  - Varun Mehra
AU  - Rajeev J. Ram
AU  - Claudio Vergara
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DO  - 10.1109/ghtc.2016.7857308
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Details

DOI: 10.1109/ghtc.2016.7857308
Countries: India
Regions: Asia
Categories: agtech, energy, rural-data-and-definitions
Added: 2026-04-28