Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model

This study explores the implementation of advanced machine learning techniques to enhance the integration of renewable energy into smart grids, focusing specifically on predicting solar power generation for the upcoming year. Three distinct machine learning models are employed: the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and a hybrid model that combines Autoencoder and Long Short-Term Memory (AE-LSTM). Using real-time solar power production data spanning a year, these models are trained and evaluated using mean absolute error (MAE) and mean squared error (MSE) as performance metrics. Results highlight the superior accuracy of the hybrid AE-LSTM model compared to the LSTM model as well as Bi-LSTM model, attributed to its capability to capture intricate temporal patterns and correlations within the data. This research underscores the significant potential of machine learning techniques, particularly the hybrid AE-LSTM approach, in facilitating the seamless integration of renewable energy resources into smart grids, contributing to more efficient and environmentally conscious power systems. Furthermore, a comprehensive analysis reveals the hybrid AE-LSTM model’s capacity to produce superior predictions due to its additional training, solidifying its advantage over models solely reliant on the other model’s architecture. In summary, this study demonstrates the effectiveness of advanced machine learning methodologies in revolutionizing renewable energy integration, with the hybrid AE-LSTM model standing out as a promising avenue for enhanced prediction accuracy.