Patel Daxal, Patel Shriya, Patel Poojan, Shah Manan
Department of Electronics and Communication Engineering, Nirma University, Ahmedabad, Gujarat, India.
Department of Computer Science and Engineering, Indus University, Ahmedabad, Gujarat, India.
Environ Sci Pollut Res Int. 2022 May;29(22):32428-32442. doi: 10.1007/s11356-022-19185-z. Epub 2022 Feb 17.
To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced technology. The most readily available source of energy is solar energy but solar energy has nonlinear nature due to the random nature of climate conditions. So, one way to solve is solar radiation prediction and solar energy prediction using more accurate techniques. Also, energy business and power system control units require more accuracy along with very short to large duration prediction in advance. So, to complete the requirement many prediction techniques are used and among them, Artificial Neural Network (ANN) and Fuzzy are more accurate and reliable techniques. In this paper basically, a literature study for solar radiation and energy prediction using ANN and Fuzzy logic techniques has been carried out. Many studies are reviewed and then selected some most accurate, reliable, and relevant studies for further study. ANN models with different algorithms such as feed-forward back-propagation-based ANN, Multi-layer feed-forward-based ANN model, Linear regression with ANN model, GNN-based model are reviewed in the study. ANN models with different input parameters combinations and the different number of neurons were also reviewed. Fuzzy logic-based and Adaptive Neuro-Fuzzy interface (ANFIS)-based different models have been reviewed and observed that the ANFIS technique performs better. From the study, it has been noted that ANN and Fuzzy logic employed models are most effective for estimation than any other empirical models. It is found that solar radiation and energy prediction models are dependent on input parameters more. At last, highlighted some possible research opportunities and areas for better efficiency of the results.
为了满足全球对能源消耗的需求，我们必须利用先进技术找到一些更好、更稳定的可再生能源替代方式。最容易获得的能源是太阳能，但由于气候条件的随机性，太阳能具有非线性特性。因此，一种解决方法是使用更精确的技术进行太阳辐射预测和太阳能预测。此外，能源业务和电力系统控制单元需要更高的准确性以及提前从极短到长时间的预测。所以，为了满足这些要求，人们使用了许多预测技术，其中人工神经网络（ANN）和模糊逻辑是更准确、更可靠的技术。本文主要对利用人工神经网络和模糊逻辑技术进行太阳辐射和能源预测的文献进行了研究。对许多研究进行了综述，然后选择了一些最准确、最可靠且相关的研究进行进一步探讨。研究中综述了具有不同算法的人工神经网络模型，如基于前馈反向传播的人工神经网络、基于多层前馈的人工神经网络模型、带有人工神经网络模型的线性回归、基于图神经网络的模型。还综述了具有不同输入参数组合和不同神经元数量的人工神经网络模型。对基于模糊逻辑和基于自适应神经模糊推理系统（ANFIS）的不同模型进行了综述，发现ANFIS技术表现更好。从研究中可以看出，与任何其他经验模型相比，采用人工神经网络和模糊逻辑的模型在估计方面最为有效。研究发现，太阳辐射和能源预测模型对输入参数的依赖性更强。最后，强调了一些可能的研究机会和领域，以提高结果的效率。
