Home › Entities › academia › Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Research article (International journal of energy and environmental engineering, 2022) · cited 40× · AI/ML
academia openalex:W4226148019
Press Enter · cited answer in seconds

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Summary

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery is a scholarly article[1].

Key Facts

  • Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery. Retrieved May 24, 2026, from https://4ort.xyz/entity/global-horizontal-and-direct-normal-solar-irradiance-modeling-by-the-machine-learning-methods-xgboost-and-deep-neural-ne
MLA “Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/global-horizontal-and-direct-normal-solar-irradiance-modeling-by-the-machine-learning-methods-xgboost-and-deep-neural-ne.
BibTeX @misc{4ortxyz_global-horizontal-and-direct-normal-solar-irradiance-modeling-by-the-machine-learning-methods-xgboost-and-deep-neural-ne_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery}}, year = {2026}, url = {https://4ort.xyz/entity/global-horizontal-and-direct-normal-solar-irradiance-modeling-by-the-machine-learning-methods-xgboost-and-deep-neural-ne}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery — https://4ort.xyz/entity/global-horizontal-and-direct-normal-solar-irradiance-modeling-by-the-machine-learning-methods-xgboost-and-deep-neural-ne (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/global-horizontal-and-direct-normal-solar-irradiance-modeling-by-the-machine-learning-methods-xgboost-and-deep-neural-ne · Last refreshed: