By the 1960s solar power was the standard for powering space-bound satellites. In the early 1970s, solar cell technology became cheaper and more available ($20/watt). Between 1970 and 1990, solar power became more commercially operated. Railroad crossings, oil rigs, space stations, microwave towers, aircraft, etc. Now, houses and businesses all over the world use solar cells to power electrical devices with a wide variety of uses. Solar power is the dominant technol.
[PDF Version]
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]
