PREDICT: Creating a predictive model for electricity transmission losses

In the era of rising electricity prices and the integration of renewable sources, an accurate forecast of losses on power lines is extremely important for the energy network operator. Incorrectly estimated losses cause increased network fees. Since the network fees can make up to 40% of the final electricity price for households, precise forecasts affect all electricity market participants.

The PREDICT project aims to:

design an AI-based method for fast and precise line loss forecasting

support the decision-making of network operators to increase network stability and save energy costs

Modern power systems are experiencing an increase in power demand and changes to complex interconnected power networks comprising conventional and renewable energy sources.

Maintaining a balance between generation and demand is important for the reliable operation of power networks. Besides forecasts of generation and demand, forecasts of transmission losses play an important role in the decision-making of system operators.

This project aims to design and verify transmission loss prediction models using artificial intelligence methods. Predictions will be based on historical data using regression analysis and state-of-the-art AI methods, such as support vector regression and artificial neural networks.

Model attributes, including weather characteristics, will be chosen by feature selection methods and properly preprocessed. Appropriate algorithms will shorten the time needed to get the solution, reduce operating costs and ultimately save energy costs for all electricity market participants. We are working on innovative solutions that will be responsive to changes in the energy market and will represent significant progress in this area.