This paper presents a variety of ML approaches combined with XAI to predict solar power generation, aiming to optimize energy management in smart grids. . Machine learning (ML) algorithms can provide highly accurate predictions, but their complexity often makes them difficult to interpret due to their black-box nature. Combining ML and Explainable Artificial Intelligence (XAI) makes these models more transparent and enables users to understand the. . This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts power generation rates based on environmental conditions. .
[PDF Version]
Johannesburg, South Africa — The landscape of energy consumption in Africa is undergoing a seismic shift. Affordable solar panels from Chinese manufacturers are rapidly transforming lives and economies across the continent. . In 2024, 21 African countries generated at least 5% of their electricity from solar, with 7 countries surpassing the 10% mark The global solar energy market achieved a historic milestone in 2024, adding an estimated 503 gigawatts (GW) of new capacity, bringing the total installed capacity to over 2. . Support CleanTechnica's work through a Substack subscription or on Stripe. Every year on January 1, news outlets struggle to find stories that offer hope that the new year will be better than the year that just ended. Families and businesses are seizing this opportunity to cut energy costs. .
[PDF Version]
