In this study, single and aggregated forecasters of half-hourly global horizontal irradiance are assessed. The models are the standard persistent model and four newly proposed static, dynamic, moving average, and amplified persistent models. These sub-forecasters are aggregated using equal, annual optimal, and monthly optimal weights. A particle swarm optimizer was used to find those weights. Measured data, obtained from two desert sites for the years 2015–2018, was used for fitting and training the different models, while the data of the year 2019 was used to test their prediction capabilities. For the single forecasters, the dynamic model is the most accurate, followed by the static and average models. When the aggregated model of annual optimal weights was tested, the three contributing forecasters were the dynamic, average, and amplified models. The dynamic forecaster held the largest weight due to its prediction superiority during overcast and partially cloudy days. When monthly optimal weights were used, all forecasters contributed, and the dynamic model held the largest weight during winter but not in the summer when the clear sky condition is dominant. The aggregated model was the most precise, with relative mean square errors lower than 15.0% and coefficients of determination higher than 98.8%.

在这项研究中，评估了半小时全球水平辐照度的单个和聚合预测器。这些模型是标准持久模型和四个新提出的静态、动态、移动平均和放大持久模型。这些子预测器使用相等的、年度最佳和每月最佳权重聚合。使用粒子群优化器来找到这些权重。2015-2018年从两个沙漠站点获得的实测数据用于拟合和训练不同的模型，而2019年的数据用于测试其预测能力。对于单一预测者，动态模型最准确，其次是静态模型和平均模型。当测试年度最佳权重的聚合模型时，三个贡献预测器是动态的、平均的、和放大模型。由于在阴天和部分多云的日子里具有预测优势，动态预报器的权重最大。当使用月最佳权重时，所有预报员都做出了贡献，动态模型在冬季保持最大权重，但在晴空条件占主导地位的夏季则不然。聚合模型最精确，相对均方误差低于15.0%，决定系数高于98.8%。