Solar power is growing across India and the world. Homes, offices, farms and large solar parks now use panels to produce clean electricity. But solar power has one clear limit: it depends on sunlight.
Clouds, rain, dust and changing weather can reduce power output. This makes it hard for power companies to know how much electricity a solar plant will produce the next day. A new study shows how artificial intelligence, or AI, can help solve this problem.
Why solar power forecasts matter
An electricity grid must stay balanced at all times. It needs to produce enough power to match demand. If people use more electricity than expected and solar plants produce less power, the grid may face stress. This can lead to power cuts or the need to switch to other sources.
The opposite can also cause problems. If a solar plant produces more electricity than expected, the grid must handle the extra power. Operators may need to store it, send it to another area or reduce the output from some plants.
That is why power companies create forecasts for the next day. A more accurate forecast helps them plan power supply, manage batteries and use other power plants at the right time. It also helps reduce waste and improve grid stability.
How AI reads weather and power data
Traditional forecasting systems study older data and look for patterns. They can work well when weather conditions follow a known trend. However, weather can change in ways that are hard to track with simple models.
AI models use a different approach. They can study large sets of weather and solar power data. They can also find links between sunlight, cloud cover, temperature, wind and power output.
One type of AI used in the study was an artificial neural network. These systems use layers of calculations to learn from past examples. A model called BiLSTM was also tested. It can study changes over time and use information from both earlier and later points in a data sequence.
That matters because solar power changes through the day. The weather at 9 am can affect the power available at 10 am. A sudden cloud layer can also change output within minutes. The model needs to understand these time-based links, not just one weather reading.
What the study found
Researchers used weather and solar power data collected between 2019 and 2022. They tested several forecasting models under real conditions. Some models used older forecasting methods, while others used AI-based systems.
The results showed that no single model worked best in every place and every type of weather. This is an important point. A model that performs well on a clear day may not perform as well during rain or heavy cloud cover.
The BiLSTM model performed better than the other individual models in the study. It could track changes in weather and solar output with greater accuracy. The AI system improved solar power forecasts by up to 13 per cent compared with earlier methods.
Still, the best results came from combining the output of several models. This method is known as an ensemble approach. Think of it as asking several weather experts for an answer instead of trusting one person with the entire forecast. It can reduce the effect of mistakes made by any one model.
Two methods improved the forecast
The researchers tested two main ways to combine information.
Weighted averaging: In this method, models with a better past record receive more importance. If one model gives more accurate results in a certain location, its forecast gets a higher weight. The system still uses other models, but it does not treat every result as equal.
Multi-input forecasting: This method combines weather information from different sources. It may use data from several locations, weather stations or other monitoring systems. The AI model then studies these inputs together to create one forecast.
Using more than one source can help when a solar plant sits near changing weather. A cloud may cover one side of a plant while another side stays in sunlight. More data gives the system a better view of what may happen next.
What this could mean for India
India is adding solar capacity at a steady pace. Solar plants now form a key part of the country’s clean energy plans. Better forecasts can help grid operators manage this growth.
Accurate predictions can support battery storage, power trading and backup planning. They may also help distribution companies reduce sudden gaps between demand and supply. For households with rooftop solar, better forecasting could improve the use of home batteries and reduce the need to draw power from the grid at costly times.
Forecasting systems still need good data, regular testing and local weather information. A model trained in one region may not work as well in another. Hills, coastlines, pollution and local cloud patterns can all affect solar output. Power companies will need to test these systems across seasons and locations before using them for daily grid decisions.
