Low Earth Orbit (LEO) satellite equipped with image inference capabilities (LEO-IISat) offer significant potential for Earth Observation (EO) missions. However, the dual challenges of limited computational capacity and unbalanced energy supply present significant obstacles. This paper introduces the Accuracy-Energy Efficiency (AEE) index to quantify inference accuracy unit of energy consumption and evaluate the inference performance of LEO-IISat. It also proposes a lightweight and adaptive image inference strategy utilizing the Markov Decision Process (MDP) and Deep Q Network (DQN), which dynamically optimizes model selection to balance accuracy and energy efficiency under varying conditions. Simulations demonstrate a 31.3% improvement in inference performance compared to a fixed model strategy at the same energy consumption, achieving a maximum inference accuracy of 91.8% and an average inference accuracy of 89.1%. Compared to MDP-Policy Gradient and MDP-Q Learning strategies, the proposed strategy improves the AEE by 12.2% and 6.09%, respectively.

Low Earth orbit (LEO) satellite networks (LSNs) are essential for comprehensive 6G coverage. However, the high energy consumption presents a significant challenge. This letter proposes a green energy management strategy, data prediction‐energy matching (DP‐EM) for LEO satellites. An index, average energy matching error, is proposed to evaluate the energy matching error per time slot. Simulations demonstrate that DP‐EM reduces AEME by 31.59%, 64.83% and 75.00% compared to greedy algorithm, game theory and particle swarm optimisation, respectively.

This paper proposes a green business aggregation strategy tailored to low Earth orbit (LEO) satellite networks (LSNs), dynamically adjusting satellite states to address energy limitations inherent to LEO satellites. A three-layer network architecture is introduced, with control satellites, service satellites, and user devices working collaboratively. Utilizing a Markov Decision Process (MDP) and a Double Deep Q-Network (Double DQN), this strategy optimizes business scheduling and satellite state management. Simulation results validate that the proposed aggregation strategy saves up to 47.87\% energy under low traffic and 4.36\% under high traffic, underscoring its effectiveness.

This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy–Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.87% under low business volumes, accompanied by a minimal delay increase of only 0.0161 s. For medium business volumes, energy savings reach 26.75%, with a delay increase of 0.0189 s, while high business volumes achieve a 4.36% energy reduction and a delay increase of 0.0299 s. These results highlight the proposed strategy’s ability to effectively balance energy efficiency and delay, showcasing its adaptability and suitability for sustainable operations in LEO satellite networks under varying traffic loads.