More profits with reliable forecasts of energy production from renewables – Tradea case study

By September 21, 2021November 5th, 2021Artificial Intelligence, Energy, R & D
renewables case study

Implementing the 4RES system at Tradea helped to reduce Tradea’s balancing market participation costs thanks to reliable weather forecasts provided for 250 distributed solar and wind energy sources, using hybrid models utilizing artificial intelligence/ machine learning (AI/ML) and analytical computing.

Tradea is an independent, licensed electric energy trading company operating on the Polish and European energy market. The company was established in 2010.

In late 2019, Globema and Tradea partnered to develop optimal methods for forecasting energy production in solar and wind farms.

tradea

Tradea’s evaluation of our research was positive and together we decided to launch the service, based on 4RES, from the start of 2021. We have created these reliable forecasts using AI/ML.

Check out the case study to learn:

  • What prompted Tradea to implement a system of forecasts for renewable energy production
  • How Globema took advantage of AI/ML models to forecast renewable energy production
  • How the 4RES system addressed the challenge of managing the costs of participation in the balancing market

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