With the rapid expansion of cloud computing infrastructure, energy consumption has become a critical challenge, driving the need for accurate and efficient prediction models. This study proposes a novel Vector Weighted Average Kernel Extreme Learning Machine (VWAA-KELM) model to enhance energy consumption prediction in cloud computing environments. By integrating a vector weighted average algorithm (VWAA) with kernel extreme learning machine (KELM), the proposed model dynamically adjusts feature weights and optimizes kernel functions, significantly improving prediction accuracy and generalization. Experimental results demonstrate the superior performance of VWAA-KELM: 94.7% of test set prediction errors fall within [0, 50] units, with only three cases exceeding 100 units, indicating strong stability. The model achieves a coefficient of determination (R2) of 0.987 in the training set (RMSE = 28.108, RPD = 8.872) and maintains excellent generalization with R2 = 0.973 in the test set (RMSE = 43.227, RPD = 6.202). Visual analysis confirms that predicted values closely align with actual energy consumption trends, avoiding overfitting while capturing nonlinear dependencies. A key innovation of this study is the introduction of adaptive feature weighting, allowing the model to dynamically assign importance to different input parameters, thereby enhancing high-dimensional data processing. This advancement provides a scalable and efficient approach for optimizing cloud data center energy consumption. Beyond cloud computing, the proposed hybrid framework has broader applications in Internet of Things (IoT) and edge computing, supporting real-time energy management and intelligent resource allocation.

翻译：随着云计算基础设施的快速扩张，能耗已成为关键挑战，推动了对精准高效预测模型的需求。本研究提出了一种新型的向量加权平均核极限学习机（VWAA-KELM）模型，以提升云计算环境中的能耗预测能力。通过将向量加权平均算法（VWAA）与核极限学习机（KELM）相结合，所提模型能够动态调整特征权重并优化核函数，显著提高了预测精度与泛化性能。实验结果表明VWAA-KELM具有优越性能：测试集预测误差的94.7%落在[0, 50]单位区间内，仅有三例超过100单位，显示出强稳定性。该模型在训练集上决定系数（R2）达到0.987（RMSE = 28.108，RPD = 8.872），并在测试集保持优异泛化能力（R2 = 0.973，RMSE = 43.227，RPD = 6.202）。可视化分析证实预测值与实际能耗趋势高度吻合，在捕捉非线性依赖关系的同时避免了过拟合。本研究的关键创新在于引入自适应特征加权机制，使模型能够动态分配不同输入参数的重要性，从而提升高维数据处理能力。这一进展为优化云数据中心能耗提供了可扩展的高效方法。除云计算外，所提出的混合框架在物联网（IoT）与边缘计算领域具有更广泛的应用前景，可支持实时能耗管理与智能资源分配。