Enhanced solar power forecasting in smart grids using a hybrid autoencoder and long short-term memory model

This study investigates the integration of solar energy into smart grids using artificial intelligence (AI) to improve energy management and production control. Accurate forecasting of key parameters enhances solar power generation efficiency and reduces losses, supporting the transition from traditional power systems. Initially, two deep learning models gated recurrent unit network (GRUNet) and long short-term memory network (LSTMNet) were evaluated. LSTMNet demonstrated superior performance across error metrics: Mean absolute percentage error (MAPE), mean absolute error, and mean squared error. To further enhance forecasting accuracy, two hybrid models were developed: Hybrid convolutional neural networks with long short-term memory net (HCLNet: a convolutional neural network and long short-term memory (LSTM) combination) and hybrid autoencoder LSTMNet (HAELNet: an autoencoder-LSTM framework). These models were trained and validated using one year of real solar power plant data. Results showed that HAELNet outperformed HCLNet, achieving the lowest MAPE values for daily power generation, grid connected power generation, and solar radiance of 1.221, 2.282, and 2.131, respectively. HAELNet's improved accuracy is attributed to its ability to capture complex patterns and long-term dependencies in time-series data. The study emphasizes the critical role of machine learning in the evolving energy sector and it's potential to support sustainability goals by optimizing renewable energy forecasting and reducing greenhouse gas emissions. Overall, the findings highlight the value of advanced AI models for efficient and reliable solar energy integration.