Advanced Forecasting of Wind Energy Generation Through Integration of AE-CLSTM

The advent of sustainable energy solutions marks a pivotal shift in the global energy landscape, with wind energy playing a crucial role due to its minimal environmental impact. However, the inherent unpredictability of wind necessitates advanced forecasting methods for effective grid management. This research introduces the Autoencoder Convolutional Long Short-Term Memory (AE-CLSTM) model, a novel approach that synergizes the dimensionality reduction prowess of Autoencoders with the spatial and temporal pattern recognition capabilities of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This integrated model aims to enhance the accuracy and reliability of wind energy forecasting, addressing the challenges posed by the stochastic nature of wind behavior. Employing an extensive dataset from a SCADA system of the wind farm, the AE-CLSTM model outperforms conventional CNN Model, LSTM Model, and CNN-LSTM model in terms of accuracy. The key findings of this research are encouraging: the model achieved a Correlation Coefficient of 95.43%, indicating a strong positive relationship between predicted and actual energy outputs, signifying the model's effectiveness in providing reliable forecasts than other models. These outcomes highlight the potential of AE-CLSTM in transforming wind power prediction, contributing significantly to the effective management of smart grids and the expansion of sustainable energy integration.