Autonomous exploration enables unmanned aerial vehicles (UAVs) to map unknown environments without human intervention. While state-of-the-art algorithms primarily focus on maximizing coverage or minimizing exploration time, they often overlook energy efficiency, a critical constraint for battery-powered UAVs. This thesis introduces EAAE, a modular Energy-Aware Autonomous Exploration framework that explicitly incorporates energy consumption into the exploration decision-making process. By combining frontier detection, a K-means divisive clustering algorithm, energy-aware target selection, and dual-layer planning, the framework balances information gain with energy cost. The algorithm is evaluated in simulation using the Agilicious control stack, with a physically realistic power model based on rotor speeds. Two 3D environments of varying complexity are used to compare EAAE against two baselines, an information-gain-based frontier method and distance-based frontier methods. Results show that EAAE consistently achieves the lowest total energy consumption while maintaining competitive or lower exploration times. Moreover, it demonstrates similar performance in terms of information entropy and reduced power variability, contributing to more efficient and robust mapping. This work highlights the importance of integrating energy-awareness into exploration pipelines and provides a foundation for further research into long-endurance autonomous aerial missions.