Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation

TL;DR

This paper uses symbolic regression and genetic programming to analyze weather variables affecting wind farm energy output, providing a reliable predictive model based on publicly available data.

Contribution

It introduces a novel application of symbolic regression to identify key weather parameters and their interactions for accurate wind energy prediction.

Findings

Identified important weather variables influencing energy output

Developed a reliable predictive model for wind farm energy based on weather data

Revealed correlations among weather parameters and energy production

Abstract

Wind energy plays an increasing role in the supply of energy world-wide. The energy output of a wind farm is highly dependent on the weather condition present at the wind farm. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproductions. With this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We reveal the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly given weather data.