Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm

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The current paper investigates several methods of predicting wind energy generation for the onshore “La Haute Borne” wind farm. The hybrid model has been developed to get short-term power forecasts using both historical in-situ measurements available from ENGIE and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global reanalysis dataset.Meteorology is connected with chemistry because weather is the state of air, determined by pressure and temperature variations. This means that enriching historical measurements with weather data has a potential to get better predictions. It was shown that adding three extra meteorological parameters – pressure, humidity, and temperature – allowed to reach a higher accuracy compared with cases when weather parameters were completely ignored or used partially; this was proved by applying multivariate, one-step Long Short-Term Memory (LSTM) networks.Finally, the paper explains how to apply LSTM networks for the day-ahead forecasts. In particular, two state-of-the-art models were investigated – a base LSTM network and a more advanced method which combines convolutional and LSTM layers through the CNN-LSTM approach. Results showed the latter network reached higher accuracy for both 12- and 24-h forecasts and performed faster than an ordinary LSTM network. A significant advantage of both methods deals with their light structure which allows running models on the Central Processing Unit (CPU).