Renewable Energy load forecasting is a critical process in efficiently managing electrical substations, ensuring a reliable power supply, optimizing costs, and informing strategic infrastructure planning. This chapter investigates the application of ANN in energy load forecasting, providing a structured overview of fundamental concepts, methodologies, and advancements in the field. It discusses the significance of load forecasting and the importance of renewable Energy load forecasting, its types, and the key factors influencing forecasting accuracy, and explores ANN-based models, highlighting their architectures, algorithms, and training methodologies, including feedforward, recurrent, and convolutional networks. There are many ML methods for accurately forecasting the power demand, but hybrid models perform better with ML techniques. A comparative analysis of single and hybrid models underscores the benefits of hybrid approaches in improving forecasting accuracy by combining multiple predictive techniques. A hybrid model is produced by combining two or more predictive models, each of which improves performance based on its algorithm. Case studies are presented to demonstrate real-world applications, showcasing the capability of ANNs to handle complex datasets and outperform traditional methods. This chapter also addresses common challenges, such as data preprocessing, overfitting, and computational constraints, and proposes potential solutions. Drawing from approximately sixty published research studies between 2011 and 2024, and also consolidates advancements in ANN architectures and emphasizes the role of real-time data integration in future research. It is imperative for decision-makers and analysts within the power sector to accurately predict future electricity demand with a high degree of precision and minimal error. This capability is essential for ensuring a continuous and reliable power supply to consumers, thereby minimizing the occurrence of load shedding. Simply, it concludes that accurate predictions can lead to improved reliability, reduced operational costs, and enhanced planning for future infrastructure requirements. The findings aim to guide researchers and practitioners in leveraging ANN-based techniques to meet the growing demands of the energy sector, minimize errors, and enhance system reliability. Additionally, they provide information on solar winds and renewable energy forecasting and emphasize the importance of green energy utilization.