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History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining
•More than 1000 publications on the topic of solar forecasting are reviewed and analyzed.•A text mining approach is used to provide thorough literature review automatically.•A group of domain experts jointly evaluate and interpret the text mining results.•Most, if not all, aspects of solar forecasti...
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Published in: | Solar energy 2018-07, Vol.168, p.60-101 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •More than 1000 publications on the topic of solar forecasting are reviewed and analyzed.•A text mining approach is used to provide thorough literature review automatically.•A group of domain experts jointly evaluate and interpret the text mining results.•Most, if not all, aspects of solar forecasting are discussed in details.
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. |
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ISSN: | 0038-092X 1471-1257 |
DOI: | 10.1016/j.solener.2017.11.023 |