Abstract Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

中文翻译：

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太阳辐照度和光伏功率预测的历史和趋势：使用文本挖掘的初步评估和回顾

摘要 文本挖掘是推动学术文献综述的新兴话题。本文初步研究了如何使用文本挖掘审查太阳辐照度和光伏 (PV) 功率预测（这两个主题合并为“太阳能预测”），这是即将推出的一系列文本挖掘应用程序的第一部分。太阳预报。本研究包含三个主要贡献：（1）通过谷歌学术搜索返回的前 1000 篇论文建立太阳能预测的技术基础设施（作者、期刊和会议、出版物和组织）；(2)通过对249篇ScienceDirect出版物全文的挖掘，整合太阳预报中常用的缩写；(3) 确定太阳能预测最新进展中的关键创新（例如，影子相机，预测和解）。由于上述分析中涉及的大部分步骤都是通过应用程序编程接口自动化的，因此可以通过更改搜索词将所提出的方法转移到其他太阳能工程主题或任何其他科学领域。作者承认，在现阶段，文本挖掘是对传统评论论文的补充，而不是替代。