论文标题
与贝叶斯优化算法集成的机器学习对管状太阳能的预测仍然
Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm
论文作者
论文摘要
提出的是利用两种机器学习（ML）技术的管状太阳能（TSS）生产力的新一代预测模型，即：随机森林（RF）和人工神经网络（ANN）。根据在埃及气候下记录的实验数据进行预测模型。气象和操作热参数被用作输入层。此外，贝叶斯优化算法（BOA）用于获得RF和ANN模型的最佳性能。此外，将这些模型结果与多线性回归（MLR）模型的结果进行了比较。因此，在实验上，平均值累积的生产率为4.3 l/（m2day）。对于模型结果，与ANN相比，RF对ANN的性能的敏感性不大，因为BOA可以显着提高RF。此外，RF在当前数据集上实现了TSS的更好预测性能。 RF和ANN的测定系数（R2）分别为0.9964和0.9977，远高于MLR模型，为0.9431。根据鲁棒性能和高精度，建议将RF作为预测TSS生产率的稳定方法。
Presented is a new generation prediction model of a tubular solar still (TSS) productivity utilizing two machine learning (ML) techniques, namely:Random forest (RF) and Artificial neural network (ANN). Prediction models were conducted based on experimental data recorded under Egyptian climate. Meteorological and operational thermal parameters were utilized as input layers. Moreover, Bayesian optimization algorithm (BOA) was used to obtain the optimal performance of RF and ANN models. In addition, these models results were compared to those of a multilinear regression (MLR) model. As resulted, experimentally, the average value accumulated productivity was 4.3 L/(m2day). For models results, RF was less sensitive to hyper parameters than ANN as ANN performance could be significantly improved by BOA more than RF. In addition, RF achieved better prediction performance of TSS on the current dataset. The determination coefficients (R2) of RF and ANN were 0.9964 and 0.9977, respectively, which were much higher than MLR models, 0.9431. Based on the robustness performance and high accuracy, RF is recommended as a stable method for predicting the productivity of TSS.
