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Forecasting electricity consumption of India through nighttime satellite imagery

Author

Listed:
  • Darshini R.
  • Akshay Kumar
  • Maria Anu Vensuslaus
  • Rishikeshan C. A.
  • Joshua Thomas John Victor

Abstract

Amidst a growing need for effective energy management, government policies increasingly rely on accurate electricity consumption forecasts to make informed decisions on renewable energy adoption. This study investigates the predictive capabilities of night light satellite imagery in forecasting electricity usage in India, aligning with Sustainable Development Goals 7 and 10. Utilizing data from the VIIRS satellite and NASA’s Black Marble product, the research employs various LSTM models to analyse electricity consumption trends. Additionally, state-wise analyses have been conducted by applying k-means clustering to capture spatial consumption variations. By demonstrating the strong correlation between night lights and electricity consumption, the study emphasizes the utility of satellite imagery for actionable insights into energy dynamics. The results emphasize the viability of night light data as a dependable indicator of electricity demand, with MAPE values below 10% and RMSE values below 20 MU. It also highlights the transformative impact of remote sensing technologies in advancing sustainable development agendas and highlights the pivotal role of night light imagery in energy forecasting initiatives.

Suggested Citation

  • Darshini R. & Akshay Kumar & Maria Anu Vensuslaus & Rishikeshan C. A. & Joshua Thomas John Victor, 2025. "Forecasting electricity consumption of India through nighttime satellite imagery," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-42, September.
    • Handle: RePEc:plo:pone00:0327031
    DOI: 10.1371/journal.pone.0327031
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    References listed on IDEAS

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    1. Giacomo Falchetta & Michel Noussan, 2019. "Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region," Energies, MDPI, vol. 12(3), pages 1-20, January.
    2. Li, Shuyi & Cheng, Liang & Liu, Xiaoqiang & Mao, Junya & Wu, Jie & Li, Manchun, 2019. "City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data," Energy, Elsevier, vol. 189(C).
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