The parameters of the photovoltaic cells and panels are very important to forecast the power generated. There are a lot of methods to extract the parameters using analytical, metaheuristic, and hybrid algorithms. The comparison between the widely used analytical method and some of the best metaheuristic algorithms from the algorithm families is made for datasets from the specialized literature, using the following statistical tests: absolute error, root mean square error, and the coefficient of determination. The equivalent circuit and mathematical model considered is the single diode model. The result comparison shows that the metaheuristic algorithms have the best performance in almost all cases, and only for the genetic algorithm, there are poorer results for one chosen photovoltaic cell. The parameters of the photovoltaic cells and panels and also the current-voltage characteristic for real outdoor weather conditions are forecasted using the parameters calculated with the best method: one for analytical—the five-parameter analytical method—and one for the metaheuristic algorithms—hybrid successive discretization algorithm. Additionally, the genetic algorithm is used. The forecast current-voltage characteristic is compared with the one measured in real sunlight conditions, and the best results are obtained in the case of a hybrid successive discretization algorithm. The maximum power forecast using the calculated parameters with the five-parameter method is the best, and the error in comparison with the measured ones is 0.48%.

中文翻译：

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提取光伏电池和面板参数的分析方法与元启发式方法

光伏电池和面板的参数对于预测发电量非常重要。有很多方法可以使用解析算法、元启发式算法和混合算法来提取参数。广泛使用的分析方法与算法系列中的一些最佳元启发式算法之间的比较是针对专业文献中的数据集进行的，使用以下统计检验：绝对误差、均方根误差和确定系数。考虑的等效电路和数学模型是单二极管模型。结果比较表明，元启发式算法几乎在所有情况下都具有最佳性能，仅遗传算法对于所选的一种光伏电池的结果较差。使用最佳方法计算的参数预测光伏电池和面板的参数以及实际室外天气条件的电流 - 电压特性：一种用于分析 - 五参数分析方法 - 一种用于元启发式算法 - 混合连续离散化算法。此外，还使用了遗传算法。将预测的电流电压特性与在真实阳光条件下测量的电流电压特性进行比较，在混合连续离散化算法的情况下获得了最佳结果。五参数法计算参数的最大功率预测效果最好，与实测值相比误差为0.48%。