Optimizing solar power generation forecasting in smart grids: a hybrid convolutional neural network -autoencoder long short-term memory approach

Abstract

Incorporating zero-carbon emission sources of energy into the electric grid is essential to meet the growing energy needs in public and industrial sectors. Smart grids, with their cutting-edge sensing and communication technologies, provide an effective approach to integrating renewable energy resources and managing power systems efficiently. Improving solar energy efficiency remains a challenge within smart grid infrastructures. Nonetheless, recent progress in artificial intelligence (AI) techniques presents promising opportunities to improve energy production control and management. In this study, initially, we employed two different machine learning models: Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM), to forecast solar power plant parameters. The analysis revealed that the LSTM model performed better than RNN in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Following a review of the LSTM model's graphical results, it was further enhanced by combining Autoencoder with LSTM, creating the Autoencoder LSTM (AELSTM) model. Next, a new hybrid model was introduced: Convolutional Neural Network-Autoencoder Long Short-Term Memory (HCAELSTM), designed to boost prediction accuracy. These models were trained on a one-year real-time solar power plant dataset for training and performance assessment. Ultimately, the hybrid HCAELSTM model surpassed the AELSTM model in terms of MAPE, MAE, and MSE. It excelled in MAPE scores for Daily Power Production, Peak Grid Power Production, and Solar Radiance, achieving low scores of 1.175, 2.116, and 1.592 respectively, demonstrating superior accuracy. The study underscores the importance of AI and machine learning, in particular, the hybrid model HCAELSTM, in enhancing the smart grid's ability to integrate renewable energy sources. The hybrid model excels at accurately forecasting key measurements, improving solar power generation efficiency within the smart grid system which also plays a key role in the broader shift toward the fourth energy revolution.