A team of Ghanaian researchers is harnessing artificial intelligence and advanced computational methods to engineer more efficient, cost-effective, and environmentally friendly solar photovoltaic panels, opening up new pathways for expanded renewable energy adoption across the country.
Spearheaded by academics from the Kwame Nkrumah University of Science and Technology (KNUST) and the University of Ghana under the French Embassy-funded NKABOM project, the initiative utilizes machine learning algorithms to simulate and identify optimal material combinations for next-generation solar cells. By streamlining the discovery process for stable lead-free perovskites and high-performance organic solar cells, the research aims to elevate photovoltaic conversion efficiency from standard commercial levels of roughly 22 percent to an impressive 25 to 30 percent. Project leads noted that these computational breakthroughs significantly reduce the time and financial resources traditionally required for exhaustive laboratory experimentation.
Beyond boosting power output, the project explores flexible solar technologies that can be seamlessly integrated into curved architectural designs, everyday clothing, and portable devices while minimizing material toxicity. Energy experts emphasized that successfully translating these simulated designs into scalable, locally manufactured hardware could drastically lower energy costs for households and commercial enterprises, providing a vital boost to rural electrification and Ghana’s broader transition toward sustainable energy independence.
Source: joyfmonline.com