Imagine if you had a secret weapon—an on-site power generation system—that not only keeps the lights on, but also saves your business from excess charges and improves your energy efficiency. But what exactly is on-site power generation, and how does it work?. As organizations explore on-site options, solar energy is an attractive solution for most sustainable energy strategies. But, as organizations look to scale operations and maximize the power output, barriers to success arise. Implementing an on-site solar program often requires the organization to. . Reduce utility costs, achieve energy independence and meet your sustainability goals by generating your own on-site power–and even selling surplus energy back to the grid.
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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. .
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