The integration of Artificial Intelligence (AI) into solar energy systems is revolutionizing the management of renewable energy generation, optimization, and distribution. Solar energy, with its intermittent nature, presents significant challenges in forecasting, load balancing, and power optimization. AI-based models, particularly machine learning (ML) and deep learning (DL), have emerged as powerful tools in addressing these issues, offering enhanced predictive accuracy, real-time decision-making, and efficient energy management. This chapter explores the current advancements in AI-driven solar energy forecasting, focusing on the application of AI models to predict solar radiation and power generation. Furthermore, it delves into real-time load balancing strategies, emphasizing the role of reinforcement learning and optimization algorithms in managing energy distribution. The chapter also examines the potential of AI in improving power optimization, integrating solar energy with storage systems, and enhancing grid stability. Despite the considerable progress made, challenges remain in terms of computational efficiency, scalability, and the integration of hybrid energy systems. This chapter provides a comprehensive overview of AI's transformative impact on solar energy, offering insights into future research directions and practical applications. The findings highlight the promising potential of AI to drive a sustainable energy future, optimizing both the generation and consumption of solar power.
The global transition toward renewable energy is being driven by the urgent need to combat climate change and reduce dependency on fossil fuels [1]. Among the renewable energy sources, solar power has gained significant attention due to its abundance, scalability, and low environmental impact [2]. However, the integration of solar power into existing energy systems presents numerous challenges, primarily due to its intermittent and variable nature [3]. Solar energy generation fluctuates depending on weather conditions, time of day, and seasonal variations, leading to substantial inconsistencies in energy availability [4]. This variability makes it difficult to ensure a stable and reliable energy supply from solar power alone. The integration of Artificial Intelligence (AI) into solar energy systems has proven to be a game-changer, providing innovative solutions to optimize energy forecasting, load balancing, and power distribution [5].
AI-based models, particularly machine learning (ML) and deep learning (DL), are increasingly being used to enhance the accuracy of solar energy forecasting [6]. These techniques enable more precise predictions of solar radiation and power generation, which are essential for effective grid management [7]. Traditional methods of solar forecasting often rely on statistical models or simple historical data, but AI-driven approaches can process vast amounts of data from multiple sources, such as weather forecasts, real-time solar irradiance, and environmental factors [8]. By analyzing this data, AI models can make highly accurate short-term and long-term predictions, reducing the uncertainty associated with solar energy generation [9]. Improved forecasting accuracy is crucial for optimizing solar power utilization, minimizing reliance on non-renewable backup sources, and reducing the risk of energy shortages [10].
In forecasting, AI is playing a key role in addressing the challenges of load balancing and energy storage management [11]. Solar energy systems, particularly decentralized ones like microgrids and rooftop installations, often face the issue of balancing supply and demand in real-time [12]. When solar generation exceeds demand, surplus energy needs to be stored for use when generation is low, such as during cloudy days or nighttime [13]. Existing storage solutions are often limited in capacity, efficiency, and cost-effectiveness. AI-based optimization algorithms, such as reinforcement learning (RL) and fuzzy logic, can dynamically adjust the load balancing process and optimize the charging and discharging cycles of energy storage systems [14]. These real-time, data-driven solutions allow for more efficient management of energy resources, ensuring that surplus energy is stored effectively and minimizing the risk of energy wastage [15].
