Recent advancements in solar energy technology have gained significant attention, particularly with the integration of artificial intelligence. A study led by Chaouki Ghenai from the Sustainable and Renewable Energy Engineering Department at the University of Sharjah has developed predictive models using artificial neural networks (ANN) to enhance the performance of solar photovoltaic (PV) systems and electric vehicle (EV) charging stations. This research, published in “Case Studies in Thermal Engineering,” focuses on short-term forecasting of solar PV power output and the state of charge of batteries used in solar-powered EV charging stations.
The study is particularly relevant as it addresses the growing demand for sustainable energy solutions in the transportation sector. By utilizing a 3D energy model that emphasizes decarbonization, digitalization, and decentralization, Ghenai and his team aim to facilitate a shift toward more sustainable energy sources. The experimental setup included solar PV panels, inverters, and a battery bank, complemented by smart meters that collected real-time performance data.
One of the key findings of the research is the high accuracy of the ANN models in predicting solar power output and battery charge levels. The correlation coefficients (R values) for the prediction models ranged impressively between 0.9957 and 0.9969 for solar PV power, and even higher for battery state of charge, reaching up to 0.9996. Ghenai noted, “Artificial neural network (ANN) models possess considerable promise for practical implementations as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios.” This level of accuracy is crucial for balancing the supply from solar PV systems with the demand from electric vehicles.
The commercial implications of this research are significant. As the demand for electric vehicles continues to rise, the need for efficient and reliable charging solutions becomes paramount. Predictive modeling can optimize the operation and maintenance of solar-powered charging stations, ensuring they meet the energy demands of EV users. Additionally, Ghenai’s team envisions future developments including an energy management system to enhance charging efficiency, the establishment of blockchain networks for better energy transaction transparency, and the integration of cybersecurity protocols to protect these systems.
This research not only highlights the potential of artificial intelligence in energy management but also opens up new opportunities for businesses in the renewable energy sector. As companies look to invest in sustainable technologies, the ability to forecast energy production and consumption accurately can lead to more strategic planning and resource allocation, ultimately driving the transition to a greener energy landscape.
The findings from this study underscore the importance of innovative approaches in the energy sector, paving the way for smarter, more efficient systems that align with global sustainability goals.
