In a significant stride towards harnessing solar energy in Northern Cameroon, researchers have employed advanced LSTM (Long Short-Term Memory) neural networks to predict solar irradiation and temperature, key factors in optimizing photovoltaic energy production. This innovative approach, led by Saito Douswekreo from the Laboratory of Mechatronics, Energetronics and Sustainable Mobility, reveals promising potential for solar energy applications, particularly for electric vehicles (EVs).
The study utilized a robust database sourced from MERRA-2, encompassing 23 years of meteorological data from 20 locations in Northern Cameroon. By analyzing variables such as temperature, relative humidity, and solar irradiation, the researchers developed a predictive model that achieved an impressive accuracy of 92.45%. “Our goal was to create a tool that could accurately forecast solar energy potential, which is crucial for planning solar power installations and managing energy resources effectively,” Douswekreo explained.
The findings indicate that the average solar energy potential in Makary reaches 2.193 MWh/m²/year, while Banyo presents a slightly lower potential of 1.949 MWh/m²/year. This data is not merely academic; it has profound commercial implications. As the world shifts towards sustainable energy solutions, the ability to predict solar energy output can significantly influence where and how solar power plants are constructed. Moreover, with the rise of electric vehicles, these insights are invaluable for developing efficient EV fleet management systems and strategically placing charging stations.
The research highlights a growing trend in the energy sector: the intersection of artificial intelligence and renewable energy. By leveraging machine learning techniques, energy providers can enhance the reliability of solar energy, making it a more attractive option for investors and consumers alike. Douswekreo remarked, “As we look to the future, integrating AI with renewable energy resources will not only improve efficiency but also help mitigate the impacts of climate change.”
This groundbreaking study, published in the Journal of Engineering, underscores the importance of data-driven approaches in the transition to renewable energy. The implications extend beyond Cameroon, offering a blueprint for other regions to optimize their solar energy resources. As countries worldwide strive to meet ambitious climate goals, research like this could be pivotal in shaping a sustainable energy landscape that benefits both the environment and the economy. For more information, visit the Laboratory of Mechatronics, Energetronics and Sustainable Mobility.