用高斯耦合自适应集成，解决电力负荷预测中的数据稀疏问题。
Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting
- 通过高斯耦合补全缺失数据，缓解采集不确定性影响。
- 融合五种机器学习模型，提升预测鲁棒性。
- 自适应加权集成策略，适合数据不完整场景下的负荷预测。
机器学习在完整数据下可实现精准负荷预测，但数据采集常受不确定性影响导致稀疏。本文提出自适应集成学习结合高斯耦合模型，包含三个模块：数据补全、机器学习构建与自适应集成。首先，利用高斯耦合消除数据稀疏性；其次，采用五种机器学习模型独立预测；最后，通过自适应集成获得加权求和结果。实验表明，该模型具有良好的鲁棒性，适用于存在数据缺失的负荷预测任务。
原文摘要 · Abstract (English)
Machine learning (ML) is capable of accurate Load Forecasting from complete data. However, there are many uncertainties that affect data collection, leading to sparsity. This article proposed a model called Adaptive Ensemble Learning with Gaussian Copula to deal with sparsity, which contains three modules: data complementation, ML construction, and adaptive ensemble. First, it applies Gaussian Copula to eliminate sparsity. Then, we utilise five ML models to make predictions individually. Finally, it employs adaptive ensemble to get final weighted-sum result. Experiments have demonstrated that our model are robust.
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