Abstract:

As space colonization advances, sustainable and efficient energy systems are essential to support human presence beyond Earth. Solar energy, with its virtually limitless availability in space, is the ideal candidate for powering extraterrestrial habitats. Recent developments in artificial intelligence (AI) and machine learning (ML) have significantly enhanced solar energy prediction, optimization and management, offering solutions to meet the growing energy demands of space missions. This paper explores the integration of AI-driven models in solar radiation forecasting, energy storage and and decentralized power distribution for space applications. By leveraging machine learning techniques, such as neural networks and generative adversarial networks, we aim to improve the precision of solar radiation prediction, enabling more efficient energy harvesting and distribution. Furthermore, the application of AI to smart grids and energy storage technologies is crucial for ensuring continuous, reliable power supply in space habitats. The paper also discusses the potential of decentralized energy systems for adapting to dynamic environmental conditions in space. This research presents a roadmap for harnessing AI, ML and cutting-edge energy technologies to create self-sustaining, resilient energy systems that will be vital for future space colonization.