article · Scientific Reports
Solar irradiance is erratic and influenced by climatic variations, complicating renewable energy deployment in Central Africa. To improve solar power forecasting, an artificial neural network model was trained to predict solar irradiance on an inclined surface in the metropolitan area of Douala. Meteorological measurements, including temperature, wind speed, humidity, and air pressure, were recorded at 30-minute intervals over nearly two years by a local monitoring station. Data gathered between January 2019 and October 2020 was divided into training, validation, and testing sets to evaluate different input configurations. A multilayer perceptron configuration using a logistic sigmoid activation function with 50 hidden layer neurons attained a correlation coefficient of 98.883 percent between observed and predicted values. This predictive model offers an accurate method for estimating solar radiation levels in Douala and comparable climatic zones across Central Africa.
Accurate forecasting of solar irradiance helps manage the unpredictable nature of solar energy generation caused by fluctuating weather conditions. By reliably predicting solar radiation on tilted surfaces using standard meteorological factors, energy planners can better assess local solar resources. This supports efforts to deploy solar technologies and address the broader energy shortfall in Central African urban centres and climatically comparable areas.
The model represents applied and tested computational research that could enable more reliable solar energy system planning and grid integration. Solar energy developers, utility operators, and engineering consultants operating in Central Africa could use these predictive models to evaluate photovoltaic performance in comparable climates. Because the approach relies on established meteorological variables and standard software tools, it is close to operational deployment for site assessment and solar potential analysis.
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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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DOI: 10.1038/s41598-024-54181-y
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