DEVELOPMENT OF A HYBRID ARTIFICIAL INTELLIGENCE FRAMEWORK FOR ACCURATE FORECASTING OF SOLAR POWER GENERATION USING MACHINE LEARNING ALGORITHMS AND TIME-SERIES ANALYSIS

Abstract

Accurate forecasting of solar power generation plays a crucial role in the efficient integration of solar energy into modern power grids and renewable energy systems. Traditional forecasting methods, such as statistical and physical models, often fail to capture the complex patterns and dynamic nature of solar radiation and its impact on power output. In this paper, we propose the development of a hybrid artificial intelligence (AI) framework that combines machine learning (ML) algorithms with time-series analysis to improve the accuracy and reliability of solar power generation predictions. The framework integrates several machine learning techniques, including decision trees, support vector machines (SVM), and artificial neural networks (ANN), with time-series forecasting methods such as autoregressive integrated moving average (ARIMA) and exponential smoothing to better model both short-term fluctuations and long-term trends in solar power output. By leveraging the complementary strengths of machine learning in data pattern recognition and time-series analysis in trend forecasting, the hybrid model enhances the precision of predictions under a variety of environmental conditions and temporal scales. To evaluate the performance of the proposed framework, we conducted a series of experiments using real-world solar power datasets. The results show that the hybrid model significantly outperforms traditional forecasting approaches in terms of both forecasting accuracy and robustness, particularly in capturing complex seasonal and diurnal variations in solar power generation. Moreover, the model demonstrates its ability to adapt to different locations and varying weather conditions, making it highly applicable for diverse geographical regions. The results highlight the potential of integrating AI-driven forecasting models with time-series analysis as a powerful tool for optimizing solar power generation and enhancing the management of renewable energy resources. Ultimately, this framework can serve as an essential tool for energy providers, grid operators, and policymakers to improve grid stability, reduce uncertainties in solar power predictions, and enable more efficient integration of solar energy into the existing energy infrastructure.