Meteorological Data Forecast using RNN

Meteorological Data Forecast using RNN

Stefan Balluff (Paderborn University, Paderborn, Germany), Jörg Bendfeld (Paderborn University, Paderborn, Germany) and Stefan Krauter (Paderborn University, Paderborn, Germany)
Copyright: © 2017 |Pages: 14
DOI: 10.4018/IJGHPC.2017010106
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Abstract

Gathering knowledge not only of the current but also the upcoming wind speed is getting more and more important as the experience of operating and maintaining wind turbines is increasing. Not only with regards to operation and maintenance tasks such as gearbox and generator checks but moreover due to the fact that energy providers have to sell the right amount of their converted energy at the European energy markets, the knowledge of the wind and hence electrical power of the next day is of key importance. Selling more energy as has been offered is penalized as well as offering less energy as contractually promised. In addition to that the price per offered kWh decreases in case of a surplus of energy. Achieving a forecast there are various methods in computer science: fuzzy logic, linear prediction or neural networks. This paper presents current results of wind speed forecasts using recurrent neural networks (RNN) and the gradient descent method plus a backpropagation learning algorithm. Data used has been extracted from NASA's Modern Era-Retrospective analysis for Research and Applications (MERRA) which is calculated by a GEOS-5 Earth System Modeling and Data Assimilation system. The presented results show that wind speed data can be forecasted using historical data for training the RNN. Nevertheless, the current set up system lacks robustness and can be improved further with regards to accuracy.
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Besides forecasting meteorological data using recurrent neural networks (RNN), there have been approaches solving the problem using other types of artificial neural networks or even statistical tools like linear prediction methods.

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