Abstract:
The safety and stabilisation of the electricity network will be challenged as wind power’s
share of the grid continues to rise, which will ultimately limit the scope of wind power
development. For the wind energy industry to grow sustainably, forecasting is crucial.
Power system dispatching may be done with confidence thanks to high-precision wind
power forecasting. Since wind energy is intermittent and occurs over a range of time
scales, increased wind power generation necessitates accurate wind forecasting. Numerous
models have been developed for precise wind power prediction because of its stochastic
character. Here, an accurate deep learning prediction model employing the LSTM (Long
Short-Term Memory) technique is suggested.The model generates a forecast of wind power
after being trained with real-time data from a wind farm. Two approaches, ARIMA and
Random Forest, were compared in order to ascertain this model’s efficacy. The outcome
demonstrates that the LSTM approach offers greater forecasting accuracy while requiring
less MAPE. In Python software, the LSTM, Random Forest, and ARIMA models are
implemented.
