数字农科院2.0

Evaluation Of Temperature-Based Machine Learning A.nd Empirical Models For P redicting Daily Global Solar Radiation

文献类型： 外文期刊

作者： Feng, Y; Gong, DZ; Zhang, QW; Jiang, SZ; Zhao, L; Cui, NB

作者机构：

关键词： Global solar radiation; Forecast; Empirical models; Machine learning models; Temperate continental regions

期刊名称： ENERGY CONVERSION AND MANAGEMENT

ISSN： 0196-8904

年卷期： 2019 年 198 卷

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： Accurate global solar radiation data are fundamental information for the allocation and design of solar energy systems. The current study compared different machine learning and empirical models for global solar radiation prediction only using air temperature as inputs. Four machine learning models, e.g., hybrid mind evolutionary algorithm and artificial neural network model, original artificial neural network, random forests and wavelet neural network, as well as four empirical temperature-based models (Hargreaves-Samani model, Bristow-Campbell model, Jahani model, and Fan model) were applied for prediction of daily global solar radiation in temperate continental regions of China. The results indicated the hybrid mind evolutionary algorithm and artificial neural network model provided better estimations, compared with the existing machine learning and empirical models. Thus, the temperature-based hybrid model is highly recommended to predict global solar radiation in temperate continental regions of China when only air temperature data are available. Combining the hybrid model with future air temperature forecasts, we can get the accurate information of future solar radiation, which is of great importance to management and operation of solar energy systems.

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