New AI will help increase solar energy production: how to use it

Researchers have combined machine learning with traditional methods to find the best places to install panels.

Solar power plants are affected by many different factors, so it is difficult to accurately predict generation. A group of scientists from India has developed a new method using artificial intelligence technology and described it in an article for the journal Nature.

Solar-powered photovoltaic systems are becoming increasingly popular due to their reliability and the desire of many countries to reduce their dependence on fossil fuels. However, it is impossible to accurately predict and plan their work, because solar radiation is unstable and is affected by weather conditions, geographical location, and seasonal changes.

Various statistical models, including autoregressive integrated moving averages, have traditionally been used to forecast SE output. These approaches are designed to capture patterns such as trends and seasonality, but in the case of solar energy, conditions are too variable and unpredictable.

According to scientists, reliable forecasting methods are essential for optimizing energy management, ensuring grid stability, and minimizing operating costs. To address these issues, this study presents an innovative method that integrates robust seasonal trend decomposition (RSTL) with a long-short-term memory (LSTM) neural network optimized using the adaptive Seagull optimization algorithm (ASOA).

Artificial intelligence has helped solve the problems of managing large datasets and accounting for errors. For example, long-short-term memory neural networks will analyze and accurately predict time series data. They effectively account for patterns and changes over time.

However, optimizing LSTM functions remains a challenging task and often results in unacceptable performance. Therefore, they have been combined with traditional mathematical models. The study found that this approach significantly improves forecasting accuracy.

The forecast will allow people to more accurately determine how much electricity a solar power plant will produce in the long term and whether it is worth installing it at all. It may also help find a more suitable location for solar panels.