Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
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Concerns about climate change, energy security and the volatility of the price of fossil fuels has led to an increased demand for renewable energy. With wind turbines being one of the most mature renewable energy technologies available, the global use of wind power has been growing at over 20% annually, with further adoption to be expected. As a result of the inherent variability of the wind in combination with the increased uptake, demand for accurate wind forecasting, over a wide range of time scales has also increased. We report early work as part of the EU FP7 project 'ORIGIN', which will exploit wind speed forecasting, and implement and evaluate smart-meter based energy management in 300 households in three ecovillages across Europe. The ORIGIN system will capitalise on automated weatherstation data (available cheaply) to inform predictions of the wind-turbine generated power that may be available in short term future time windows. Accurate and reliable wind-speed forecasting is essential in this enterprise. A range of different methods for wind forecasting have been developed, ranging from relatively simple time series analysis to the use of a combination of global weather forecasting, computational fluid dynamics and machine learning methods. Here we focus on the application of neural networks, without (for the time being) the use of numerical weather predictions or expensive physical modelling methods. While work of this nature has been performed before, using past wind speeds to make predictions into the future, here we explore the use of additional recent meteorological data to improve on short-term forecasting. Specifically, we employ evolved networks and explore many configurations to assess the merits of using additional features such as cloud cover, temperature and pressure, to predict future wind speed.