Optimizing Solar Power Efficiency in Smart Grids Using Hybrid Machine Learning Models for Accurate Energy Generation Prediction

Abstract

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks, driving progress in the domain. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. These models combine the power of traditional physics-based models with the flexibility and accuracy of machine learning algorithms to provide accurate and real-time energy production predictions. A more sustainable energy future is possible with the help of these models, which maximize the integration of renewable energy resources in smart networks. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing wastage and costs, these smart grids have significant benefits for the global energy landscape and help to reduce carbon emissions, increase energy security, and improve access to electricity in remote and rural areas too. But this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR %) to predict the efficiency of the models along with the root mean square error (RMSE) and mean absolute error (MAE). The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.