[Full Picture] Corrigendum to “A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques” [Renew. Energy 133 (2019) 575–592]

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Corrigendum to “A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques” [Renew. Energy 133 (2019) 575–592] - ScienceDirect

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Summary Analysis Research

Article summary:

1. The authors of a paper on solar radiation potential estimation have issued a corrigendum to correct information about previous studies in the literature review section.

2. The classification of methods for estimating global solar radiation on horizontal surfaces has been modified to include three types: deterministic models, data-driven models, and both deterministic and data-driven models.

3. Information about three papers has been corrected or supplemented, including moving some data from Table 2 to Table 1 and correcting details about whether certain models were deterministic or data-driven.

Article analysis:

The article is a corrigendum to a previously published paper on estimating solar radiation potential using machine learning techniques. The authors have corrected some information in the literature review section, specifically regarding the classification of methods used to estimate global solar radiation. The authors have modified the classification into three types: deterministic models, data-driven models, and both deterministic and data-driven models.

The authors have provided references to support their corrections, which adds credibility to their claims. However, it is unclear why these corrections were not made before the original paper was published. It raises questions about the quality control process of the journal and whether other papers may contain similar errors.

The article does not appear to have any biases or one-sided reporting. The authors are transparent about their mistakes and provide evidence to support their corrections. However, there are missing points of consideration in the original paper that are not addressed in this corrigendum. For example, there is no discussion of potential limitations or uncertainties associated with using machine learning techniques for estimating solar radiation potential.

There is also no exploration of counterarguments or alternative approaches that could be used for estimating solar radiation potential. This lack of discussion limits the scope of the paper and may prevent readers from fully understanding the complexities involved in this field.

Overall, while this corrigendum addresses some inaccuracies in the original paper, it does not provide a comprehensive analysis of all aspects related to estimating solar radiation potential. Future research should aim to address these gaps and provide a more nuanced understanding of this topic.

Topics for further research:

Limitations of using machine learning for solar radiation estimation Uncertainties in solar radiation potential estimation Alternative approaches to estimating solar radiation potential Factors affecting solar radiation potential Accuracy of solar radiation estimation models Comparison of deterministic and data-driven models for solar radiation estimation