机器学习与统计方法在太阳能预报中的比较性分析

普智勇; 夏攀; 张璐; 王硕; 王允; 闵敏

doi:10.19912/j.0254-0096.tynxb.2022-0290

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (7) : 162-167. DOI: 10.19912/j.0254-0096.tynxb.2022-0290

机器学习与统计方法在太阳能预报中的比较性分析

普智勇¹, 夏攀^2,3, 张璐⁴, 王硕^2,3, 王允¹, 闵敏^2,3

作者信息 +

COMPARATIVE ANALYSIS OF MACHINE LEARNING AND STATISTICAL METHODS IN SOLAR ENERGY PREDICTION

Pu Zhiyong¹, Xia Pan^2,3, Zhang Lu⁴, Wang Shuo^2,3, Wang Yun¹, Min Min^2,3

Author information +

文章历史 +

摘要

回顾传统太阳能预测方法的发展历程和特点,然后详细总结近几年基于先进的机器学习算法所发展的太阳能预测新方法。主要分析支持向量机、人工神经网络和长短期记忆网络算法的现阶段发展水平。分析得到基于机器学习的太阳能预测方法,其预测准确率较高、均方根误差和平均偏差误差小、预测过程耗时短、短时间能及时给出预测结果。最后,归纳传统和机器学习预测方法各自的优缺点和面临的难题。指出机器学习模型泛化能力弱（普适性弱）,易受环境因素干扰且预测过程和结果很难给出物理解释。

Abstract

This paper firstly reviews the development process and characteristics of traditional solar energy prediction methods. Then, the new solar energy prediction methods based on advanced machine learning algorithm in recent years are summarized . The current state-of-the-art progress of support vector machine, artificial neural network, and long/short-term memory network algorithms is mainly analyzed, respectively. The analysis shows that the solar energy prediction method based on machine learning has high prediction accuracy, small root mean square error and average deviation error, short prediction process time and timely prediction results. Finally, the advantages and disadvantages of traditional and machine learning prediction methods are summarized. It is pointed out that, the generalization ability of machine learning model is weak （weak universality）, it is very easy to be disturbed by external environmental factors. It is also difficult to give suitable physical explanations for their prediction process and results.

导出引用

普智勇, 夏攀, 张璐, 王硕, 王允, 闵敏. 机器学习与统计方法在太阳能预报中的比较性分析[J]. 太阳能学报. 2023, 44(7): 162-167 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0290

Pu Zhiyong, Xia Pan, Zhang Lu, Wang Shuo, Wang Yun, Min Min. COMPARATIVE ANALYSIS OF MACHINE LEARNING AND STATISTICAL METHODS IN SOLAR ENERGY PREDICTION[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 162-167 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0290

中图分类号： TM615

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