# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 1 Introduction  
Wind energy is essential in the global transition to sustainable energy, as it harnesses the power of wind, a renewable and abundant resource, to produce clean electricity without harmful emissions. Wind is a global phenomenon that countries around the world can tap into, and the versatility of wind production permits wind turbines to be deployed offshore as well as on land [1]. Wind energy is produced by wind turbines, which convert kinetic energy from wind into electrical power by using large blades that rotate a generator [2, 3]. As one of the fastest-growing sources of renewable energy, wind turbines play a significant role in reducing reliance on fossil fuels, lowering greenhouse gas emissions, and mitigating climate change [4, 5]. In the world energy arena, wind energy is the predominant renewable energy source, and wind power production more than tripled between 2010 and 2020, increasing from 198 gigawatts (GW) to 743 GW in that span [6]. Wind energy is sourced from wind farms, which feature scores of wind turbines spread out across a large area. Any effort to predict wind power production must consider the dynamics of every single windturbine in a wind farm, which involves an immense amount of data [7]. However, several challenges must be overcome to successfully predict wind power production. Among the most significant obstacles to wind power forecasting is the variability of wind [8]. Wind power is inconsistent regardless of the characteristics of the wind farm. Even if a location with a high average wind volume is selected, power output remains susceptible to factors such as the weather and the season, which can prove disruptive to the power grid supplied by the wind farm [9]. This is a significant drawback of wind energy, as the power grid relies on a consistent power input to meet the regular power needs of the customers. Furthermore, fluctuations in wind volume decrease the efficiency of the integration of wind energy into the power grid, increasing operating costs compared with those of fossil fuels [10]. However, adjustments can be made to manage changes in operating conditions if the wind power can be predicted in advance [11]. Accordingly, the ability to accurately forecast wind power production is of immense value to the wind energy sector. Accurate wind power prediction is challenging because the conditions of each wind farm are different. Wind power parameters, such as geography, wind turbine type, number of wind turbines, and weather patterns, differ between wind farms, which makes creating a single rigid prediction model impractical [10]. Prediction methods can be physical or statistical in nature. Physical models are supplied with weather data, including air pressure, humidity, water vapor concentrations, and air speed. These inputs are used to generate wind power output using thermodynamics, but the utility of these models is limited due to the need for up-to-date conditions and a high cost of local and imprecise data [12; 13]. Statistical methods involve supplying the model with historical wind conditions and power data. These data are used to train the model to forecast site-specific wind power production based on trends and patterns identified over time [13; 14]. Statistical wind power prediction methods are most effective when employing intelligent artificial intelligence (AI) algorithms featuring deep learning [15]. Those deep learning models are artificial neural networks (ANN), which include multiple hidden layers that allow the model to process inputs and determine outputs of interest, such as future wind power outputs [16]. Those deep learning models can analyze large datasets of historical weather patterns, real-time meteorological data, and turbine performance to forecast wind conditions more accurately, allowing wind farms to dynamically adjust turbine settings [17].