# Hybrid deep neural model for hourly solar irradiance forecasting

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/hybrid-deep-neural-model-for-hourly-solar-irradiance-forecasting/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Xiaoqiao Huang,Qiong Li,Yonghang Tai,Zaiqing Chen,Jun Zhang,Junsheng Shi,Bixuan Gao,Wu‐Ming Liu |
| Citations | 190 |
| DOI | 10.1016/j.renene.2021.02.161 |
| Fields | Computer Science,Engineering,Environmental Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://figshare.com/articles/journal_contribution/Hybrid_deep_neural_model_for_hourly_solar_irradiance_forecasting/20580105 |
| OpenAlex ID | https://openalex.org/W3135477757 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Yonghang Tai](https://scholariq.org/researchers/yonghang-tai/)

## Paper primary topic

- [Solar Radiation and Photovoltaics](https://scholariq.org/topics/solar-radiation-and-photovoltaics/)

## Paper topics

- [Solar Radiation and Photovoltaics](https://scholariq.org/topics/solar-radiation-and-photovoltaics/)
- [Energy Load and Power Forecasting](https://scholariq.org/topics/energy-load-and-power-forecasting/)
- [Air Quality Monitoring and Forecasting](https://scholariq.org/topics/air-quality-monitoring-and-forecasting/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.