ABSTRACT

Solar energy has a key role in producing clean and emissions-free power compare to conventional methods. However, sustainable development also requires a reliable and predictable energy source. It also needs methods to measure and predict predictable supply. The main aim of the study is to improve reliable and precise solar radiation prediction models on monthly mean daily basis using various machine learning techniques. Simple Membership Function and Fuzzy Rule Generating Technique (SMGRT), which does not require error and trial for model adjustment, is the first-choice model in this study. Experience and observations about the model will greatly reduce the volume of processing for the fuzzy SMGRT model. On the other hand, Deep Learning (DL) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have become increasingly popular in understanding nonlinear data structures and solving complex problems. Therefore, DL and ANFIS were also applied to estimate solar radiation. The data set used in the study were created using sunshine duration (s), extra-terrestrial solar radiation (H₀), relative humidity (RH), cloudiness (C), air temperature (T) and soil temperature (ST) parameters. Estimation performance of models was evaluated by using several statistical indicators which are Mean Bias Error (MBE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (R²). When the performances of the models were compared, it was seen that all three models obtained remarkable results. In addition, it was shown that the models performed well based on the metrics in the testing phase. The SMGRT model has slightly better performance than DL and ANFIS for different input combinations. SMGRT Model 1 (with inputs H₀, s, and T) shows the best statistical performance (MBE = 0.156, MSE = 1.878, RMSE = 1.371, and R² = 0.960) not only in SMGRT models but also in others.

摘要

与传统方法相比，太阳能在生产清洁无排放电力方面发挥着关键作用。然而，可持续发展还需要可靠且可预测的能源。它还需要测量和预测可预测供应的方法。该研究的主要目的是使用各种机器学习技术改进每月平均每天的可靠和精确的太阳辐射预测模型。不需要错误和试验进行模型调整的简单隶属函数和模糊规则生成技术（SMGRT）是本研究的首选模型。对模型的经验和观察将大大减少模糊 SMGRT 模型的处理量。另一方面，深度学习 (DL) 和自适应神经模糊推理系统 (ANFIS) 在理解非线性数据结构和解决复杂问题方面越来越受欢迎。因此，DL和ANFIS也被用于估计太阳辐射。研究中使用的数据集是使用日照时长创建的（s）、地外太阳辐射（H ₀）、相对湿度（RH）、云量（C）、气温（T）和土壤温度（ST）参数。通过使用几个统计指标评估模型的估计性能，这些指标是平均偏差误差 (MBE)、均方误差 (MSE)、均方根误差 (RMSE) 和相关系数 (R ²). 当比较模型的性能时，可以看到所有三个模型都获得了显着的结果。此外，根据测试阶段的指标，模型表现良好。对于不同的输入组合，SMGRT 模型的性能略优于 DL 和 ANFIS。SMGRT 模型 1（输入H ₀、s 和 T）显示出最佳统计性能（MBE = 0.156、MSE = 1.878、RMSE = 1.371 和 R ²  = 0.960），不仅在 SMGRT 模型中而且在其他模型中也是如此。