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.

翻译：将人工智能（AI）集成到软件系统中显著提升了系统能力，同时也加剧了能源需求。集成学习通过组合多个模型的预测形成单一预测，由于累积的能源消耗而加剧了这一问题。本文提出了一种新颖的模型选择方法，旨在解决实时AI集成系统中平衡模型准确性与能源消耗的挑战。我们探讨了在推理过程中减少集成内模型数量或提高模型使用效率，如何能在不显著牺牲准确性的前提下降低能源需求。本研究引入并评估了两种用于优化集成学习系统性能同时最小化能耗的模型选择策略：静态策略与动态策略。我们的结果表明，静态策略将F1分数提升至基线以上，并将平均能耗从完整集成的100%降低至62%。动态策略进一步提高了F1分数，平均能耗仅为完整集成的76%。此外，我们提出了一种平衡准确性与资源消耗的方法，能在不明显影响准确性的情况下显著降低能耗。该方法将静态策略的平均能耗从约62%降至14%，将动态策略的平均能耗从约76%降至57%。我们通过对一家大型专业服务提供商开发的运营AI系统进行的绿色AI实地研究，展示了在实时生产环境中采用能源意识模型选择策略的实际适用性。