  Integrating Artificial Intelligence (AI) into software systems has
significantly enhanced their capabilities while escalating energy demands.
Ensemble learning, combining predictions from multiple models to form a single
prediction, intensifies this problem due to cumulative energy consumption. This
paper presents a novel approach to model selection that addresses the challenge
of balancing the accuracy of AI models with their energy consumption in a live
AI ensemble system. We explore how reducing the number of models or improving
the efficiency of model usage within an ensemble during inference can reduce
energy demands without substantially sacrificing accuracy. This study
introduces and evaluates two model selection strategies, Static and Dynamic,
for optimizing ensemble learning systems performance while minimizing energy
usage. Our results demonstrate that the Static strategy improves the F1 score
beyond the baseline, reducing average energy usage from 100\% from the full
ensemble to 6\2%. The Dynamic strategy further enhances F1 scores, using on
average 76\% compared to 100% of the full ensemble. Moreover, we propose an
approach that balances accuracy with resource consumption, significantly
reducing energy usage without substantially impacting accuracy. This method
decreased the average energy usage of the Static strategy from approximately
62\% to 14\%, and for the Dynamic strategy, from around 76\% to 57\%. Our field
study of Green AI using an operational AI system developed by a large
professional services provider shows the practical applicability of adopting
energy-conscious model selection strategies in live production environments.
