# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 1 Introduction
This approach to wind power prediction is not new, as statistical time series models, such as autoregressive integrated moving average (ARIMA) and kernel extreme learning machine (KELM), are frequently used for process prediction. More recently, deep learning models, e.g., long short-term memory (LSTM), with elements, e.g., gated recurrent units (GRU), have become more prevalent in the prediction of wind power [9]. As a special case of recurrent neural networks (RNN), LSTM has greater accuracy and fewer errors in wind power prediction relative to other methods. Convolutional neural networks (CNNs) are also used in time series prediction, although limitations include CNNs' tendency to consider only ordinary feature extraction, while neglecting to capture time series modeling characteristics [18]. This results in maximum efficiency, improved grid integration, and reduced downtime of each turbine, which stabilizes energy production, enhances reliability of the grid, and reduces operational costs, which would help to fully unlock the potential of wind energy [19]. These AI models offer the potential for long-term wind power prediction, but limitations of existing models limit their utility. Numerous wind power prediction methodologies using the models introduced previously have been published, each of which has made valuable contributions to advance the field. One study exploring this area is the development of a CNN-LSTM deep learning model to perform 2-D regional short-term wind speed forecasting. This study combined a CNN and LSTM to develop a model capable of forecasting wind speed more accurately than other similar models. The geographic data from the wind farm was used to train the CNN component, while the chronological data were used to train the LSTM module. The feature extraction of the CNN and historical prediction of the LSTM were input back into the CNN to predict wind speed components, with very accurate results [20]. Another study involved developing a novel genetic LSTM (GLSTM) model for wind power forecasting. This study created a GLSTM framework that combined LSTM with a genetic algorithm (GA) for short-term wind power prediction. GA is an optimization algorithm that helps address the limitations of computing power and time that wind energy prediction models require to obtain accurate results. This added optimization increases the capability of LSTM layers, which bolsters the sequential data learning capability of the existing LSTM model. Results from this study indicated average improvements between 6% and 30% compared with existing wind power prediction models, including standard LSTM [14]. Both of these models, as well as countless others, considerably bolstered the quality of wind power forecasting in the short term. However, short-term predictions only provide a wind power output outlook of days at best, whereas accurate long-range prediction of wind power would allow turbine operators to better optimize the production of power. While traditional models, such as LSTM, GRU, RNN, and ARIMA, are effective for time series prediction, they have limitations in handling highly complex, multistep forecasting tasks of the nature needed for wind power prediction [21; 22]. Classic RNNs suffer from the vanishing gradient problem, hindering their ability to learn long-term dependencies in sequential data [23; 24]. Beyond the above studies, some Transformer-based architectures have been used for wind power forecasting as well. A Transformer-based deep neural network incorporating wavelet transform showed promising results in 6 h ahead wind power prediction [25]. Another Transformer-based model was developed to capture long-term dependencies and key information within wind data, enabling the extraction of correlations between wind farms and wind power forecasting [26]. [PERSON] et al. [27] used ensemble empirical mode decomposition to convert the original wind speed sequence from one to sixteen dimensions and a Transformer model to directly model the multidimensional wind speed data.