用自监督降维和低秩近似，让高阶多项式模型更高效精准预测电力负荷。
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting
- 通过自监督降维和低秩近似降低维度灾难与过拟合风险。
- 在新英格兰独立系统运营商数据集上优于多个主流模型，且用更少输入变量。
- 适合资源受限场景下的高精度电力负荷预测任务。
负荷预测是智能电网中的基础任务。尽管高阶多项式模型具备优良的数学基础与优化性质，但因维度灾难、过拟合及计算资源有限等问题，其在负荷预测中应用受限。本文提出低秩近似与自监督降维方法以解决上述问题，并采用基于共轭梯度的快速算法提升计算效率。基于ISO New England的负荷数据集，所提出的高阶多项式自监督降维方法（HOPS）在多个对比模型中展现出更高预测精度。实验还表明，该方法能有效减少冗余变量构建，在更少输入变量下实现更优预测效果。
原文摘要 · Abstract (English)
Load forecasting is a fundamental task in smart grid. Many techniques have been applied to developing load forecasting models. Due to the challenges such as the Curse of Dimensionality, overfitting, and limited computing resources, multivariate higher-order polynomial models have received limited attention in load forecasting, despite their desirable mathematical foundations and optimization properties. In this paper, we propose low rank approximation and self-supervised dimension reduction to address the aforementioned issues. To further improve computational efficiency, we also utilize a fast Conjugate Gradient based algorithm for the proposed polynomial models. Based on the load datasets from the ISO New England, the proposed method high-order polynomials with self-supervised dimension reduction (HOPS) demonstrates higher forecasting accuracy over several competitive models. Additionally, experimental results indicate that our approach alleviates redundant variable construction, achieving better forecasts with fewer input variables.
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