电力气象相似日数据

主要用于指导电力公司开始逐时负荷预测，它综合考虑了逐时负荷对气温的响应阈值、高影响气象因子对负荷影响的累积效应和滞后效应、电力负荷周期性等特征，建立了包括随机森林算法在内的组合预测模型。它可根据预测要素，自动计算与预测日负荷曲线相似的5个日期，并估算预测日与各气象相似日的气象负荷、时间增量差异。

This dataset is primarily designed to guide power companies in conducting hourly load forecasting. It comprehensively takes into account multiple characteristics including the response threshold of hourly load to temperature, the cumulative and lag effects of high-impact meteorological factors on load, and the periodicity of electric power load, and establishes a combined forecasting model that includes the Random Forest algorithm. It can automatically calculate five dates with similar load curves to the forecast day based on the forecasting factors, and estimate the meteorological load and time increment differences between the forecast day and each meteorologically similar date.