Evidence map›Paper›PMID 40997079›Full record

ArticlePloS one2025

Forecasting electricity consumption of India through nighttime satellite imagery.

Darshini R, Akshay Kumar, Maria Anu Vensuslaus, Rishikeshan C A, Joshua Thomas John Victor

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Darshini RStudent, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Akshay KumarStudent, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.ORCID 0009-0002-6107-6597
Maria Anu VensuslausFaculty, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.ORCID 0000-0002-5712-7761
Rishikeshan C AFaculty, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Joshua Thomas John VictorFaculty, Engineering Computing & Built Environment, UOW Malaysia KDU University College, Shah Alam, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ElectricitySatellite ImageryForecastingIndia

Identifiers

PMID40997079
PMCPMC12463292

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.