Weather forecasts have become extremely effective and precise thanks to development of more and more sophisticated weather models. Availability of precise weather data and forecasts has motivated us to look for patterns and dependencies of weather features to wind energy outputs.
In this case we analyzed several months of data from an Australian wind farm in Tansania together with the publicly available weather data measured on the same location. Robust predictive models helped us iteratively identify the MINIMAL LIST OF WEATHER FEATURES impacting the energy output.
Initially 16 features were used to predict energy output, out of which ONLY TWO FEATURES - a wind gust and a dew point were identified as drivers with the quantified contribution of each factor to the accuracy of predictions.
"...the presented framework is so simple that it can be used literally by everybody for predicting wind energy production on a smaller scale—for individual wind mills on private farms or urban buildings, or small wind farms.
Models were developed using the data collected from October 2010 to June 2011. Their predictions however were tested on the weather data collected in July 2011 (an entirely unseen season). The results showed a very good prediction accuracy (of 12% RMSE).
Related Links/ Publications/ Resources:
Ekaterina Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagner – Predicting the energy output of wind farms based on weather data: Important variables and their correlation.
Renewable Energy Journal, 2013, vol 50, p.236-243