用加权平均优化极端学习机，精准预测云数据中心能耗。
Cloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm Improved by Vector Weighted Average Algorithm
- 融合向量加权平均与核极限学习机，动态调整特征权重。
- 测试集94.7%误差在50单位内，训练集R²达0.987，泛化能力强。
- 适合需要高精度能耗预测的云中心与物联网场景。
随着云计算基础设施快速扩展，能源消耗成为关键挑战，亟需高效准确的预测模型。本文提出一种新型向量加权平均核极限学习机（VWAA-KELM）模型，通过将向量加权平均算法（VWAA）与核极限学习机（KELM）结合，动态调整特征权重并优化核函数，显著提升预测精度与泛化能力。实验结果表明，测试集中94.7%的预测误差落在[0, 50]单位内，仅3例超过100单位，表现出强稳定性；训练集上R²达0.987（RMSE=28.108，RPD=8.872），测试集保持R²=0.973（RMSE=43.227，RPD=6.202）。可视化分析显示预测值紧密跟随实际能耗趋势，未出现过拟合，有效捕捉非线性依赖关系。核心创新在于自适应特征加权机制，可动态赋予不同输入参数重要性，增强对高维数据的处理能力。该方法为优化云数据中心能耗提供可扩展、高效解决方案，亦适用于物联网（IoT）与边缘计算中的实时能源管理与智能资源分配。
原文摘要 · Abstract (English)
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.
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