IBM uses machine learning to increase the productivity of solar plants.

One of the famous use-cases of Machine Learning and Artificial Intelligence in today's era is to enhance the productivity of renewable energy resources. With the accurate forecasting of weather, machine learning can benefit in two ways,

  1. It will indirectly forecast the energy production in different months,
  2. It will optimize the production by providing exact locations for solar plants.

The department of energies from different tech-advanced countries like America have partnered with IBM to utilize and develop its ML technology – Watt-Sun – which sorts through data gathered from a substantial bank of weather reports.

This partnership aims to reduce the uncertainty in the variable energy production from solar power plants. Watt-Sun was able to improve the accuracy of weather prediction by up to 30% from the conventional methods.

Yes, it's indeed true that the variability of the sunlight can not be controlled; still, with the predictive ability of these technologies, we can easily utilize other options of other energy resources. When the predicted production would be higher, we can avoid the usage of non-renewable energy resources and benefit the environment.

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