Application of hybrid deep learning models for seasonal solar irradiance forecasting in the city of Pala
Year: 2026
Type: article
Abstract: Accurate prediction of solar irradiance is crucial for designing, controlling, and monitoring solar energy systems. However, its intermittent nature poses a significant challenge to achieving satisfactory forecasting results. This study aims to design new hybrid models and evaluate the performance of different forecasting models. The models combine Attentive Meteo-Embedding with long-term memory (AME-LSTM), Attentive Meteo-Embedding and convolutional neural networks (AME-CNN), Attentive Meteo-Embedding and gated recurrent units (AME-GRU) using the Adam optimization algorithm. The models take temperature, relative humidity, and wind direction as input variables for the years 2015–2023. The results show that the AME-GRU model performs exceptionally well in summer and spring, while the AME-CNN model excels in summer and autumn. However, the AME-LSTM model has notable limitations, with higher errors in winter. This study can contribute to the development of a hybrid power plant for the city of Pala.
Source: Discover Applied Sciences
Institutions: Université de Yaoundé I, Université de Dschang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7171258100