02058nas a2200205   4500000000100000000000100001008004100002260001200043100002100055700001600076700002000092700002400112700002000136245009100156856005800247300000900305490001300314520151100327022001401838       9998                            d  c07/20241 aManisha Galphade1 aV. B. Nikam1 aBiplab Banerjee1 aArvind W. Kiwelekar1 aPriyanka Sharma00aStacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data  uhttps://www.ijimai.org/journal/bibcite/reference/3460  a1-110 vIn press3 aCurrently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid operators to enhance safety and optimize grid operations by effectively managing fluctuations in power generation, ensuring a reliable and stable energy supply. In recent years, there has been a significant rise in research and investigations conducted in this field. This study aims to develop a multivariate short-term wind power forecasting (WPF) model with the objective of enhancing forecasting precision. Among the various prediction models, deep learning models such as Long Short-Term Memory (LSTM) have demonstrated outstanding performance in the field of WPF. By adding multiple layers of LSTM networks, the model can capture more complex patterns. To improve the performance, data preprocessing is carried out using two techniques such as removal of missing values and imputing missing values using Random Forest Regressor (RFR). The comparison between the proposed Stacked LSTM model and other methods including vector autoregressive (VAR), Multiple Linear Regression, Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM) has been experimented on two datasets. The experimental results show that after imputing missing values using RFR, the Stacked LSTM is optimized model for better performance than above mentioned reference models.  a1989-1660