Challenge

For small photovoltaic systems (<30kWp), CKW currently only measures the energy fed into its grid. This creates a gap in understanding the actual energy production and self-consumption of these systems. The objective of this challenge is to bridge that gap by developing a machine learning model that can accurately estimate total energy production and self-consumption.

Develop a machine learning model to estimate the energy production of small photovoltaic (PV) systems (<30kWp) using feed-in data, weather information, and egid data. The goal is to enable innovative and intelligent grid- and market-oriented solar energy solutions.

Data

• CKW’s grid includes approximately 9,600 PV systems smaller than 30kWp without production measurement.
• Participants will work with:
  • Time series energy data
  • Weather data (provided by Meteomatics)
  • EGID data
• The dataset is preprocessed with basic feature engineering, allowing participants to focus on building the best possible model.
• Both traditional machine learning approaches and advanced neural networks, such as LSTMs, are encouraged.

Why it matters for CKW

This challenge has significant real-world implications:

• Starting next year, PV production will be limited to 70% of the panel’s peak output as per new regulations.
• Accurate production estimates will help:
  • Quantify energy losses due to these limits.
  • Support the creation of innovative, market-oriented solar energy products.
  • Enhance CKW’s ability to make solar energy more attractive and efficient for customers.

Contact

Enrique Romano, CKW