This paper develops a novel short-term load forecasting model that hybridizes several machine learning methods, such as support vector regression (SVR), grey catastrophe (GC (1,1)), and random forest (RF) modeling. The modeling process is based on the minimization of both SVR and risk. GC is used to process and extract catastrophe points in the long term to reduce randomness. RF is used to optimize forecasting performance by exploiting its superior optimization capability. The proposed SVR-GC-RF model has higher forecasting accuracy (MAPE values are 6.35% and 6.21%, respectively) using electric loads from Australian-Energy-Market-Operator; it can provide analytical support to forecast electricity consumption accurately.

本文开发了一种新的短期负载预测模型，该模型混合了多种机器学习方法，例如支持向量回归 (SVR)、灰色灾难 (GC (1,1)) 和随机森林 (RF) 建模。建模过程基于 SVR 和风险的最小化。GC用于长期处理和提取突变点，以减少随机性。RF 用于通过利用其卓越的优化能力来优化预测性能。所提出的SVR-GC-RF模型在使用来自Australian-Energy-Market-Operator的电力负荷时具有更高的预测精度（MAPE值分别为6.35%和6.21%）；可为准确预测用电量提供分析支持。