Published Paper Details:

This research focuses on the modeling of energy demand predictability, along with the integration of renewable sources of energy, such as wind and solar through hybrid approaches. This study seeks to develop a new hybrid predictive model for energy demand forecasting with precise and effective predictions through the integration of traditional energy forecasting techniques and renewable energy sources, including wind and solar. The model will address the challenges posed by the intermittent and erratic nature of renewable energy generation, assessing how hybrid strategies can help stabilize the grid while optimizing energy distribution. The study seeks to improve the accuracy and adaptability of energy demand forecasts using advanced artificial intelligence and machine learning techniques, enabling more reliable integration of renewable energy sources. Moreover, the study will examine the economic and environmental impacts of integrating energy storage systems with smart grid technology and hybrid energy models. This will be conducted to improve resource optimization while minimizing reliance on fossil fuels. The entire study will be based on an all-inclusive framework for the implementation of hybrids forecasting models using different approaches such as statistical, machine learning, and physical methodologies to improve the reliability, sustainability, and efficiency of energy systems. In summary, the study focuses on developing sustainable, resilient, and economically feasible energy systems, which are part of the shift toward renewable sources but ensure stability on the grid as well as environmentally friendly effects. A framework with data-based considerations to aid the transition to more sustainable and secure energy systems.