Invited lecture2024
Harnessing Machine Learning for Improved Solar Radiation Prediction
- Affordable and Clean Energy
- Industry, Innovation and Infrastructure
- Partnerships for the Goals
- Thriving and Sustainable Economy
The record
- Type
- Invited lecture
- Date
- 9 August 2024
- Level
- International — held in India
- Location
- Bengaluru, Karnataka, India
- Schools
- Reporting cycle
- 2024
- Programme
- NMITCON 2024
- Organiser
- NITTE, Bengaluru
- People presenting
- 1
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
7.2 Substantially increase the share of renewable energy in the global energy mix
Tier B rule renewable-energy matched "solar" in the record’s own title. Names a renewable energy source or its technology, which is target 7.2.
- Certain confidence
- The record states the subject matter explicitly. A reader shown only the title and this goal would agree without hesitation. Example: a workshop titled "Renewable Energy and Smart Grid" against SDG 7.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- tier-b-lexicon-v2, under kortex-input-v1+rubric@1.1.0#7bc5877481c9+lexicon@tier-b-lexicon-v2#fb739e066356Input template kortex-input-v1; rubric 1.1.0; lexicon tier-b-lexicon-v2. Each can change without the others, which is why the version is a composite.
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Tier B rule computing-and-ai matched "machine learning" in the record’s own title. Names computing, data or artificial intelligence as the method or the subject. Target 9.5 unless an applied domain also fires, in which case the domain takes the primary and this takes the secondary.
- 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.
- Produced by
- tier-b-lexicon-v2, under kortex-input-v1+rubric@1.1.0#7bc5877481c9+lexicon@tier-b-lexicon-v2#fb739e066356Input template kortex-input-v1; rubric 1.1.0; lexicon tier-b-lexicon-v2. Each can change without the others, which is why the version is a composite.
17.16 Enhance the global partnership for sustainable development, complemented by multi-stakeholder partnerships
Structural signal: an international-level designation with no named counterpart.
- 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
- sdg17-structural-v1, under kortex-input-v1+rubric@1.1.0#7bc5877481c9+lexicon@tier-b-lexicon-v2#fb739e066356Input template kortex-input-v1; rubric 1.1.0; lexicon tier-b-lexicon-v2. Each can change without the others, which is why the version is a composite.
Viksit Bharat @2047
vb-2 Thriving and Sustainable Economy
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 7 label. Tier B rule renewable-energy matched "solar" in the record’s own title. Names a renewable energy source or its technology, which is target 7.2.
- Produced by
- tier-b-lexicon-v2, under kortex-input-v1+rubric@1.1.0#7bc5877481c9+lexicon@tier-b-lexicon-v2#fb739e066356Input template kortex-input-v1; rubric 1.1.0; lexicon tier-b-lexicon-v2. Each can change without the others, which is why the version is a composite.
Records of this type were classified by a published, versioned rule set, over 5,246 activities. Each label above carries the rubric version it was produced under. How each classification is reached.
Evidence
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No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.
Provenance
Which source system this record came from, how many source rows were resolved into it, and when each was last read.
- Source rows resolved into this record
- 2
- Source records held
- 2
2 rows across the university’s source registers were resolved into this one activity. Where those rows disagreed on a value, the disagreeing values are retained alongside the record and are never shown as a combined string.
| Source system | Reference | Last read |
|---|---|---|
| KIIT PBAS reporting workbook, 2024 cycle | Invited Lecture - 2024!84 | |
| KIIT PBAS reporting workbook, 2024 cycle | Invited Lecture - 2024!446 |
Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.
Citing this record
This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.
KIIT Deemed to be University. “Harnessing Machine Learning for Improved Solar Radiation Prediction”. Kortex. Last read 17 September 2026.