Publication2026
Enhanced real time electricity consumption forecasting using a hybrid deep learning model for greenhouse
The record
- Type
- Publication
- Date
- 1 December 2026
- Level
- National
- Journal
- Franklin Open
- Volume
- 17
- Published
- 1 December 2026
- DOI
- 10.1016/j.fraope.2026.100737
- Scopus ID
- 105048659690
- ISSN
- 27731871
- Authors
- 4
- Open access
- Yes
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
7 Ensure access to affordable, reliable, sustainable and modern energy for all
Hybrid deep learning framework CBLNet for real-time electricity consumption forecasting in greenhouse systems, enabling data-driven energy management and sustainable agricultural operations
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
2 End hunger, achieve food security and improved nutrition and promote sustainable agriculture
Smart greenhouse energy forecasting supports sustainable food production and agricultural efficiency
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
Smart greenhouse technology supporting sustainable agricultural production and rural economy applications
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Enhanced real time electricity consumption forecasting using a hybrid deep learning model for greenhouse”. Kortex.