Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions

Laveet Kumar; Sohrab Khan; Faheemullah Shaikh; Mokhi Maan Siddiqui; Ahmad K Sleiti

doi:10.1093/ce/zkae114

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Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions

更新时间：2026-02-06

- Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions
- Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions
- 清洁能源（英文） 2025年第2期
- 作者机构：
  
  1. Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University
  2. Department of Electrical and Computer Engineering, Mohammad Ali Jinnah University
  3. Department of Electrical Engineering, Mehran University of Engineering & Technology
- 作者简介：
- 基金信息：
  
  Open access funding is provided by Qatar National Library.
- DOI：10.1093/ce/zkae114
  中图分类号：
- 纸质出版：2025
- 稿件说明：
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Laveet Kumar, Sohrab Khan, Faheemullah Shaikh, 等. Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions[J]. 清洁能源（英文）, 2025,(2).

Laveet Kumar, Sohrab Khan, Faheemullah Shaikh, Mokhi Maan Siddiqui, Ahmad K Sleiti, Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions, Clean Energy, Volume 9, Issue 2, April 2025, Pages 150–160, https://doi.org/10.1093/ce/zkae114
Laveet Kumar, Sohrab Khan, Faheemullah Shaikh, 等. Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions[J]. 清洁能源（英文）, 2025,(2). DOI： 10.1093/ce/zkae114.

Laveet Kumar, Sohrab Khan, Faheemullah Shaikh, Mokhi Maan Siddiqui, Ahmad K Sleiti, Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions, Clean Energy, Volume 9, Issue 2, April 2025, Pages 150–160, https://doi.org/10.1093/ce/zkae114 DOI：

摘要

Abstract

Solar photovoltaic energy generation due to its high potential is being adopted as one of the main power sources by many countries to mitigate their climate and electrical power issues. Hence accurate forecasting becomes important to make grid operations smoother

and for this purpose

modern-day artificial intelligence technologies can make a significant contribution. This study is an endeavor to target accurate forecasting for different weather conditions by using a simple recurrent neural network

long–short-term memory and gated recurrent unit-based hybrid model

and bidirectional gated recurrent unit. The experimental dataset has been acquired from Quaid-e-Azam Solar Park

Bahawalpur

Pakistan. This study observed that the bidirectional gated recurrent unit outperforms the hybrid model

whereas the simple recurrent neural network lags most in accuracy. The results confirm that the bidirectional gated recurrent unit technique can perform accurately in all critical weather types. Whereas the values of root-mean-square error

mean absolute error

and R-squared values also ensure the precision of the model for all weather conditions

and the best of these parameters for bidirectional gated recurrent unit observed are 0.0012

0.212

and 0.99

respectively

for the overcast dataset.

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