As the integration of Machine Learning (ML) models becomes pervasive in software systems, the associated energy costs have emerged as a critical concern. This research delves into the energy efficiency of ML-enabled systems, focusing on the ML component itself and its impact on the overall system. Our primary emphasis is on the data-centric approach, particularly in the context of feature selection and handling concept drift, and how these energy-efficient components affect the overall energy consumption of ML-enabled systems. In our initial investigation, we explored feature selection methods and identified significant variations in their energy consumption. This led us to delve deeper into understanding how different techniques for scoring features contribute to the overall energy footprint of ML models. Subsequently, we examined the impact of changes in data distribution, often referred to as concept drift, on model accuracy and the associated energy costs. Our empirical experiments revealed noteworthy insights into energy-efficient strategies for handling concept drift, a crucial aspect of maintaining ML-enabled systems. We compared various methods and their effectiveness in mitigating the adverse effects of concept drift while keeping energy consumption in check. The findings from our research contribute to the development of sustainable and energy-efficient ML models within the broader context of software engineering. Lastly, we will compare how different alternatives of ML components in ML-enabled systems affect the overall energy consumption of ML-enabled systems.