In a significant advancement for renewable energy, researchers led by Saito Douswekreo from the Laboratory of Mechatronics, Energetronics, and Sustainable Mobility have unveiled a groundbreaking method to predict solar energy potential in northern Cameroon. Their study, published in the Journal of Engineering, harnesses the power of Long Short-Term Memory (LSTM) neural networks to forecast critical parameters affecting photovoltaic production, namely solar irradiation and temperature.
This innovative approach is particularly timely, as the global push for sustainable energy sources intensifies amidst the ongoing climate crisis. The research utilizes a comprehensive database from MERRA-2, which encompasses 23 years of meteorological data from 20 locations in northern Cameroon. This data includes temperature, humidity, atmospheric pressure, wind speed, and solar irradiation, all measured hourly. The LSTM model developed by Douswekreo and his team achieved an impressive accuracy of 92.45% and a root mean square error (RMSE) of just 20.9, indicating a robust predictive capability.
The implications of this research are profound for the construction sector, particularly in the burgeoning field of solar energy infrastructure. By estimating the average solar energy potential at 2.193 MWh/m2/year in Makary and 1.949 MWh/m2/year in Banyo, the findings provide critical insights for developers looking to establish solar power plants and electric vehicle (EV) charging stations. “This model not only aids in site selection for solar installations but also optimizes energy management for solar photovoltaic systems,” Douswekreo stated, emphasizing the model’s practical applications.
As the construction industry increasingly pivots towards sustainability, this research paves the way for more informed decision-making regarding the placement of solar infrastructure. The ability to accurately predict solar energy availability could significantly enhance the operational efficiency of solar electric vehicle fleets, a sector poised for rapid growth as the world transitions to greener transportation solutions.
Moreover, the findings from this study could catalyze investments in renewable energy projects across the region, stimulating local economies and contributing to a reduction in carbon emissions. As the demand for sustainable energy solutions rises, understanding localized solar potential becomes crucial for both developers and policymakers.
The research not only underscores the importance of advanced technologies like LSTM neural networks in energy forecasting but also highlights the role of data-driven decision-making in the construction and energy sectors. As the industry embraces these innovations, the potential for a more sustainable future becomes increasingly tangible.
For further insights into this transformative research, you can visit the Laboratory of Mechatronics, Energetronics and Sustainable Mobility.
