Solar Irradiance Forecasting Using Deep Learning Techniques †

1Citations
Citations of this article
21Readers
Mendeley users who have this article in their library.

Abstract

Solar irradiance, the power of sunlight received on a given surface area during a specific time, is crucial in determining the efficiency and performance of solar power systems, as it directly influences the electricity units generated by photovoltaic (PV) cells. In recent years, deep learning and machine learning techniques have been leveraged to enhance the accuracy of solar adsorption and wind power forecasting. In this context, this study presents a comparative study of various deep learning models for very short term solar irradiance forecasting, aiming to find the most effective model for this specific purpose for our local city Karachi. The key findings indicate that the LSTM model outperforms the other architectures, achieving the highest R-squared value and the lowest RMSE. These results emphasize the importance of accurate forecasting models in optimizing renewable energy generation and grid management and their potential applications in various sectors.

References Powered by Scopus

Gate-variants of Gated Recurrent Unit (GRU) neural networks

1274Citations
N/AReaders
Get full text

A review on the long short-term memory model

962Citations
N/AReaders
Get full text

Short-Term photovoltaic power forecasting based on long short term memory neural network and attention mechanism

302Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

9Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Khan, H. A., Alam, M., Rizvi, H. A., & Munir, A. (2023). Solar Irradiance Forecasting Using Deep Learning Techniques †. Engineering Proceedings, 46(1). https://doi.org/10.3390/engproc2023046015

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 2

100%

Readers' Discipline

Tooltip

Engineering 1

50%

Earth and Planetary Sciences 1

50%

Article Metrics

Tooltip
Mentions
News Mentions: 1

Save time finding and organizing research with Mendeley

Sign up for free