Solar radiation forecasting plays a significant role in precisely designing solar energy systems and in the efficient management of solar energy plants. Most research only focuses on accuracy improvements; however, for an effective forecasting model, considering only accuracy or stability is inadequate. To solve this problem, a combined model based on nondominated sorting-based multiobjective bat algorithm (NSMOBA) is developed for the optimization of weight coefficients of each model to achieve high accuracy and stability results simultaneously. In addition, a statistical method and data mining-based approach are used to determine the input variables for constructing the combined model. Monthly average solar radiation and meteorological variables from six datasets in the U.S. collected for case studies were used to assess the comprehensive performance (both in accuracy and stability) of the proposed combined model. The simulation in four experiments demonstrated the following: (a) the proposed combined model is suitable for providing accurate and stable solar radiation forecasting; (b) the combined model exhibits a more competitive forecasting performance than the individual models by using the advantage of each model; (c) the NSMOBA is an efficient algorithm for providing accurate forecasting results and improving the stability where the single bat algorithm is insufficient.

太阳辐射预测在精确设计太阳能系统以及对太阳能发电厂的有效管理中发挥着重要作用。大多数研究只关注准确性的提高。但是，对于有效的预测模型，仅考虑准确性或稳定性是不够的。为了解决这个问题，开发了一种基于非支配排序的多目标蝙蝠算法（NSMOBA）的组合模型，以优化每个模型的权重系数，以同时实现高精度和稳定性结果。另外，使用统计方法和基于数据挖掘的方法来确定用于构建组合模型的输入变量。来自美国六个数据集的每月平均太阳辐射和气象变量 为案例研究收集的数据用于评估所提出的组合模型的综合性能（准确性和稳定性）。在四个实验中的仿真表明：（a）所提出的组合模型适用于提供准确和稳定的太阳辐射预报；（b）通过利用每种模型的优势，组合模型的预测性能比单个模型更具竞争力；（c）NSMOBA是一种有效的算法，可在单蝙蝠算法不足的情况下提供准确的预测结果并提高稳定性。（b）通过利用每种模型的优势，组合模型的预测性能比单个模型更具竞争力；（c）NSMOBA是一种有效的算法，可在单蝙蝠算法不足的情况下提供准确的预测结果并提高稳定性。（b）通过利用每种模型的优势，组合模型的预测性能比单个模型更具竞争力；（c）NSMOBA是一种有效的算法，可在单蝙蝠算法不足的情况下提供准确的预测结果并提高稳定性。