Greenlogy: Forecasting one day ahead solar panels energy supply to the grid

The aim of our collaboration  with Greenlogy within the Hopero project was to create a predictive model for one day ahead solar panels energy supply to the grid as recorded by smart meters. We tested the state-of-the-art machine learning approaches and brought improvements for models to better fit the addressed task.

Greenlogy is a Slovak company founded in 2021. Its main objective is to supply electric power generated exclusively from alternative resources and preferably by Slovak producers. 

Together with green transition, solar energy is a fast growing source of electric power in the whole world. However, solar energy is by its nature an unstable source and is highly dependent on meteorological factors. These instabilities may result in grid disruptions and difficulties in market electricity value estimation, which makes precise predictions of solar power production and its supply to the grid crucial.

The first step of the project was to identify the factors influencing the households production and consumption. Using the statistical methods we identified the most important weather and solar position parameters together with static categorical parameters such as day of the week, national or school holiday.

As a prediction model we used artificial neural networks, extending the state-of-the-art architecture by new layers to better adapt to the addressed problem. The final model was trained to deal with the principal above-mentioned factors and the historical production and consumption. The robustness of the model was supported by extensive data augmentation.

We evaluated the collaboration with Greenlogy as successful and beneficial for both participants and after the presentation of final results Greenlogy decided to put the trained model into operation. We will stay in touch to see the improvements reached by our method and are happy for another successful collaboration, under Hopero project. 

IMG_4942

Thanks to the cooperation with KInIT, we are one step closer to strategic planning of electricity production from photovoltaic sources. By linking weather predictions, we can better respond to upcoming production fluctuations. We look forward to further cooperation and involvement of artificial intelligence.

Peter Kalman

CEO, Greenlogy

Project team

Michal Sandanus
Research Engineer
Gabriela Grmanová
Researcher
Viera Rozinajová
Lead and Researcher