Industry: Energy & Renewables
Who cares: Investors, Site owners, Energy Suppliers
What Enabled: Timely prediction and revenue forecasting
Wind energy is currently a key player in the area of renewable energy. An accurate and timely prediction of it's output is critical at the stage of validating an investment decision to build a new wind farm, as well as in the energy load balancing for coordinating production of traditional power plants and weather-dependent sites.
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.
We developed predictive models using the data from October 2010 to June 2011, but heir predictions were tested on data collected in July 2011 (an entirely unseen season). The results showed a very good prediction accuracy (of 12% on rooted mean squared error) and allowed to forecast yields for the entire year from weather data and identify worst and best case scenarios.
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