ArticleScientific reports2024
A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- SIFA: A two-stage adaptive ensemble framework for solar irradiance forecasting using a wrapper-based feature selection and chaotic manta ray optimization.Scientific reports · 2026Article
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- Self-adaptive evolutionary neural networks for high-precision short-term electric load forecasting.Scientific reports · 2025Article
- Comparative analysis of daily global solar radiation prediction using deep learning models inputted with stochastic variables.Scientific reports · 2025Article
- Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations.Scientific reports · 2024Article
- Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources.Scientific reports · 2024Article
- Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia.Scientific reports · 2024Article
- Advanced efficient energy management strategy based on state machine control for multi-sources PV-PEMFC-batteries system.Scientific reports · 2024Article
- A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems.Scientific reports · 2024Article
- An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques.Scientific reports · 2024Article
- Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.Scientific reports · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.
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