Efficient use of electricity consumption and production data in real time, combined with accurate forecasts of electricity market developments, weather conditions, and future consumption and production profiles, enables effective asset management, resulting in energy cost savings and increased revenues.
Energy systems are becoming increasingly complex
Companies are facing rising electricity costs, geopolitically sensitive markets, the demands of the green transition, and evolving legislation. To avoid unpredictable expenses, many companies are making substantial investments in new energy systems, allowing them to adjust their own consumption and production profiles. By providing flexibility, such systems also enable companies to generate additional revenue.
With the integration of energy storage systems, cogeneration, cooling systems, solar power plants, and similar technologies, corporate energy systems are becoming increasingly complex. The solution lies in digital services for automated and efficient device management, which provide comprehensive control over a company’s electro-energy profile and reduce electricity costs, while at the same time enable the exploitation of alternative revenue streams (e.g., flexibility, trading), thereby improving the company’s operational efficiency.
"With our AI models, we generate forecasts that serve as the basis for effective decision-making and device management in corporate energy systems."
In collaboration with energy experts and electricity procurement specialists, we are developing AI-powered predictive models that support decision-making across various scenarios. These models help companies reduce costs, increase flexibility, and strengthen resilience against volatile energy markets.
Project goals
The main objective of this project is the development of predictive and optimization models as an integral part of solutions that help companies comprehensively manage electricity costs, negotiate electricity supply contracts, and monitor and optimize their energy portfolio.
The partial objectives of the project can be divided into three pillars:
- Forecasting and modeling
- Development of AI models for forecasting electricity generation from photovoltaics and other sources.
- Accurate electricity consumption forecasts across different time intervals.
- Modeling of schedules for energy systems (cooling, cogeneration, heating).
- Cost and efficiency optimization
- Dynamic optimization of grid fees and electricity costs.
- Development of AI models for peak load management and reduction.
- Balancing consumption and production profiles.
- Flexibility and market opportunities
- Support algorithms for system services.
- Advanced algorithms for decision-making and trading strategy development.
Partners
- Abelium d.o.o., Research and development
- OPTEN energija, d.o.o.
- Reduxi GmbH