Posted by: Leonard Wienholt
Category: Fachartikel

Ahead of the electricity price: successful energy trading with a future

Explore the transformative power of deep learning in renewable energy yield forecasting and how to use it as a direct marketer – for maximized profit.

Innovation at the heart of yield forecasting

The future of power generation is here, and it combines precision with innovation. As a direct marketer in the rapidly evolving electricity market, accurate yield forecasts play a crucial role in your competitiveness. Careful and precise yield forecasts are therefore essential.

An innovative approach: data models for yield calculation
The Potsdam-based company 4cast takes on this challenge with an innovative approach: the creation of multiple data models to calculate the yields of your plants. With a focus on analyzing asset-related weather conditions and forecasting energy production within the current day and for the following day, you can take full advantage of predictive analytics and optimize your balance sheet.

Machine learning, deep learning & physical models: Your tools for greater accuracy

From physical models and machine learning to deep learning and neural networks, we offer you a wide range of cutting-edge solutions tailored to the quantity and quality of available data. With these tools at your side, you can count on accurate and reliable predictions.

A comparison of different models has shown that finely tuned deep learning models deliver the most accurate predictions, followed by machine learning models. Physical models, on the other hand, are only used when the availability of historical production data is limited.

The effects of more precise yield forecasts can be considerable. For example, a wind farm with an installed capacity of 10 MW could reduce balancing energy costs by EUR 14,000 per month if a deep learning model is used instead of a persistence model.

Source: 4cast

Strong effects can also be achieved in photovoltaics. In our example, our deep learning model for marketing a solar park (10 MWp) reduced the balancing costs per month by EUR 7,800. A persistence model and the deep learning model that we use to calculate the energy yield forecasts in this case were compared.

Source: 4Cast

In conclusion, 4cast’s deep learning model delivers excellent wind power and solar power predictions that offer the highest level of accuracy and cost efficiency. It requires extensive data and careful setup and rewards you with valuable insights and even more accurate predictions.

The article was published on 16.02 on:
https://www.energie-und-management.de/nachrichten/suche/detail/dem-strompreis-voraus-erfolgreicher-energiehandel-mit-zukunft-209564
Portrait Dominique Heerwagen

Contact

Dominique Heerwagen
presse@4-cast.de

Author: Leonard Wienholt