Precise forecasting models – 50Hertz
eoda uses data mining to create reliable forecasting models to compensate for network losses.
MoreData science in the energy industry
Increased sustainability, higher availability of technical components or a more targeted customer approach: data is a key component in achieving these and many other goals in the energy industry. As data science specialists, we have been supporting companies in the energy industry for over 10 years now, using AI and machine learning to make optimum use of their ever-growing data stock.
A selection of questions we have answered in our projects in the energy sector:
How can grid loss during electricity transmission be predicted?
Combination of different analysis methods for the reliable forecast of the grid loss to be expected the next day during the transport of renewable energies. Creation of a better planning basis for the more cost-effective purchase of electricity on the electricity exchange.
How can the development of electricity prices be forecast?
By determining correlations between historical electricity price developments and news reports from the Thomson Reuters information service. This makes it possible to predict the effects of future news reports on the electricity price.
How can the expected electricity demand be forecast?
The energy turnaround requires the number of decentralized generation plants - the volatility of the energy fed into the grid is increasing. The data available through the smart meter rollout is the basis for forecasting future electricity demand with data science.
How can maintenance processes be controlled more closely to requirements?
By implementing a predictive maintenance approach. The generated data of the existing measurement technology provides information about the actual wear and tear and the remaining service life of components. Maintenance measures at extreme locations could be reduced to a minimum.
How can more suitable offers be developed for customers?
Linking all relevant information about the customers, such as their historical consumption data, in order to be able to use this knowledge to design attractive individual offers. In this way, customer churn could also be reduced.
How can a consistent data pipeline be built?
A multitude of data silos in distributed IT systems increases complexity and drastically reduces the amount of data available. By implementing high-performance ETL processes, the data silos could be broken down and the foundation for tapping the existing data potential could be laid.
