CMOS and beyond CMOS
Discover why imec is the premier R&D center for advanced logic & memory devices. anced logic & memory devices.
Connected health solutions
Explore the technologies that will power tomorrow’s wearable, implantable, ingestible and non-contact devices.
Life sciences
See how imec brings the power of chip technology to the world of healthcare.
Sensor solutions for IoT
Dive into innovative solutions for sensor networks, high speed networks and sensor technologies.
Artificial intelligence
Explore the possibilities and technologies of AI.
More expertises
Discover all our expertises.
Be the first to reap the benefits of imec’s research by joining one of our programs or starting an exclusive bilateral collaboration.
Build on our expertise for the design, prototyping and low-volume manufacturing of your innovative nanotech components and products.
Use one of imec’s mature technologies for groundbreaking applications across a multitude of industries such as healthcare, agriculture and Industry 4.0.
Venturing and startups
Kick-start your business. Launch or expand your tech company by drawing on the funds and knowhow of imec’s ecosystem of tailored venturing support.
/Job opportunities/Sensitivity analysis of short-term PV forecast accuracy for energy management applications in hybrid PV plants

Sensitivity analysis of short-term PV forecast accuracy for energy management applications in hybrid PV plants

Research & development - Genk | More than two weeks ago

You will analyse and quantify the value of imec’s short-term PV forecasting approach in the operation of hybrid power plants

imec has always been pushing the boundaries of PV cell and module technology development, resulting in ever-increasing performance and reliability figures at decreasing cost. Owing to these developments, we will soon reach a point where PV will become an abundant source of clean energy. However, due to its intermittent and non-dispatchable nature, it will present additional challenges in terms of system stability and grid congestion and will fundamentally challenge the conventional (central) way of operating a power system. Integrating and operating PV in a system-efficient way will be the next big challenge to increase penetration levels to evolve to a (near) 100% renewable energy system.


Dispatchable hybrid PV plants, i.e. large-scale solar paired with energy storage, have the potential to become the future backbone of the 21st-century grid. Smart, innovative energy management and power plant control software that communicates with the grid & market operators is an important part of that future. Such control software requires an accurate forecast of PV generation in order to define optimal storage or curtailment setpoints according to the different markets and ancillary services it is targeting (e.g. peak shaving, frequency control, ramp rate control...). In view of this emerging need, imec’s Energy System team developed and patented a new approach for short-term PV forecasting (“Nowcasting”), combining a sky dome imager with physics-based models and machine learning techniques. In contrast to more conventional approaches (e.g. satellite based), it allows to provide more fine-grain forecasts with higher accuracy under highly dynamic conditions (e.g. scattered clouds).


This thesis will analyse and quantify the added value of this Nowcasting approach for energy management applications in hybrid power plants. Targeting multiple markets and ancillary services, a sensitivity analysis will be performed to assess the impact of PV forecasting accuracy on the technical and economic performance of a hybrid PV plant.

Type of project: Thesis

Duration: 9 months

Required degree: Master of Engineering Technology, Master of Engineering Science

Required background: Electromechanical engineering, Electrotechnics/Electrical Engineering, Energy

Supervising scientist(s): For further information or for application, please contact: Joris Lemmens (

Only for self-supporting students.