A Short-term Wind Forecasting Framework using Ensemble Learning for Indian Weather Stations

Wind speed forecasting has proven to be crucial for many domains like power generation. However, it is quite convoluted and demanding due to the unpredictability of the wind. Numerous physical and statistical methods have been employed in the past by the researchers in India and over the years, these methods have been improvised. While the physical methods prove to be computationally expensive and the statistical methods represent weaker generalizations of non-linear features, there arises a need to develop much more efficient and robust approaches that can augment with the traditional methodologies. Artificial Intelligence has shown tremendous potential in providing solutions to time-series forecasting tasks, akin to the precise task that this work aims to address. Various machine learning and deep learning algorithms have been found to be a prominent choice for forecasting of various weather parameters. This work aims at implementing two machine learning algorithms, namely, Light Gradient Boosting Machine (LGBM) and Long Short-Term Memory (LSTM) network to obtain short-term forecasts of wind speed of Indian weather stations. Robust feature engineering approaches like Miss Forest to fill in the missing values, a novel feature representation for giving importance to recent data, and Fast Fourier Transform (FFT) with Digital Filters for removal of outliers and noises in the data have been used. Finally, stacking, an ensemble learning has been implemented using LGBM and LSTM as base learners and Random Forest as the metalearner. The average Root Mean Square Error (RMSE) (in m/s to tenths) obtained by individual models is high. 24-hour wind forecast errors of LGBM and LSTM are 8.19 and 21.17, whereas the forecast error using the proposed ensemble framework is 5.74.