Short term wind speed forecasting using time series techniques

Wind power is a renewable energy source that can be used in place of conventional fossil-fuel-based power. Although the integration of wind power has many benefits, the conventional electrical system requires a constant supply, i.e. the power supply ought to be equivalent to the power demand consistently. It's tough to keep this equilibrium because of the variation of the wind power output. Improving wind speed predictions is one of the solutions to the balance problem. This paper centers around short-term wind speed forecasting using time series methods. A time series is a logically ordered succession of numerical data points that can be used to study any variable that changes over time. This paper applies a variety of time series forecasting techniques - Exponential Smoothing, ARIMA, LSTM, and a novel hybrid LSTM-ARIMA model - to three different time periods of hourly measured wind speed data. The performance of the models is compared using metrics such as MSE (Mean Squared Error), RMSE (Root Mean Squared Error), NSE (Nash - Sutcliffe model efficiency coefficient), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error). The proposed LSTM-ARIMA model has the highest prediction accuracy and achieves the least error metrics at all time scales. It outperforms other architectures, achieving a MAPE of 24.78% for the 12-day scale, 9.30% for the 2-day scale, and 12.80% for the 1-day scale.

Automated summary

This paper focuses on short-term wind speed forecasting using time series methods. Various time series forecasting techniques are applied and compared using performance metrics. A novel LSTM-ARIMA model is proposed, which achieves the highest prediction accuracy and the least error metrics at all time scales.