New Energy Intelligent Solar Power Generation

How can AI predict power generation from solar and wind facilities?

AI techniques could enable prediction of power generation from solar and wind facilities by learning from historical weather data, sensor data (e.g., real-time wind speed and sunlight intensity measurements), and image and video data (e.g., satellite cloud maps) [141, 149].

Could Ai be the future of solar energy?

One promising path is integrating AI into the growing market of solar energy systems that offer clean and affordable energy to grid systems. According to the IEA, power sector investment in solar photovoltaic (PV) technology is projected to exceed $500 billion in 2024, surpassing all other generation sources combined.

Can artificial intelligence revolutionise solar energy management?

In this context, Artificial Intelligence (AI) in general and deep learning, in particular, emerge as a promising technology with significant potential to revolutionise solar energy management, primarily through the provision of accurate forecasts (Alam et al. 2022; Rai et al. 2021 ). In this regard, we postulate the following research questions.

Can machine learning improve solar power generation efficiency?

The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements. Recent advancements in artificial intelligence (AI) and the Internet of Things (IoT) have spurred innovative approaches in various domains.

Can AI-based forecasting improve the integration of solar electricity into power grids?

Through the implementation of an LSTM-based Deep Learning model, we have demonstrated that AI-based forecasting can significantly optimise the integration of solar electricity into power grids.

How can AI improve weather and solar generation forecasting?

Here are just a few examples. AI is emerging as a game-changer in weather and solar generation forecasting. AI algorithms analyze meteorological data to generate precise forecasts, maximizing solar output and improving grid management. This allows solar operators to better plan and mitigate the impact of intermittent energy supply.