基于气象数据可视化降维和多模加权组合的短期负荷预测

针对短期负荷预测中数据预处理的必要性和单一预测模型的局限性，提出了一种基于气象数据可视化降维和多模加权组合的短期负荷预测方法。该方法将可视化降维、模态分解降噪、单一预测模型和权重确定理论相结合，构建了气象数据降维、历史负荷分解、模态分量降噪和多模加权组合的短期负荷预测模型。通过设置3种对比实验环境，对某地区供电公司所提供的电力负荷和气象数据进行分析。预测结果及误差分析表明，所提短期负荷预测方法在保留高维气象因素本质特征结构的同时，能有效结合数据预处理方法及单一预测模型的特点，有效提升该地区电网负荷的预测精度。

Short-term Load Forecasting Based on Visualization Dimension Reduction of Meteorological Data and Multi-model Weighted Combination

Data pre-processing in short-term load forecasting and the limitations of a single forecasting model are pressing issues that need to be addressed. Thus, we propose a short-term load forecasting method based on visualization dimension reduction of meteorological data and a multi-mode weighed combination. The method combines visualization dimension reduction, modal decomposition in noise reduction, single forecasting model, and weight determination theory to design a short-term load forecasting model with dimensionality reduction of meteorological data, historical load decomposition, modal component in noise reduction, and multi-mode weighed combination. Therefore, by setting up three comparative experimental environments and analyzing the electric load and meteorological data provided by a regional power supply company, the prediction results and error analysis reveal that the proposed short-term load prediction method effectively combines the characteristics of data pre-processing methods and single prediction models while retaining the essential characteristic structure of high-dimensional meteorological factors. Furthermore, it effectively improves the prediction accuracy of the grid load in the region.