History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

By Dazhi Yang, Jan Kleissl, Christian A. Gueymard, H. Pedro, Carlos F. M. Coimbra. Solar Energy, 2018

474 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/23a7ac3c8a335f15f1ef14a4a5350b3bd69b98a0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hossein Hassani
2. Maedeh Taj Mazinani
3. Stephane Unger
4. H. Zareipour
5. Pierre Pinson
6. Christina Beneki
7. Dazhi Yang
8. Mohammad Reza Yeganegi

In Electrical and Electronic Engineering

1. Rafal Weron
2. Tao Hong
3. H. Zareipour
4. Pierre Pinson
5. Dazhi Yang
6. Yi Wang
7. Florian Ziel
8. Jakub Nowotarski

In Renewable Energy, Sustainability and the Environment

1. Pierre Pinson
2. Dazhi Yang
3. Tao Hong
4. Rafal Weron

Citations in the corpus

• Energy Forecasting: A Review and Outlook (2020, background): “After a booming 5 years, a text-mining based review was conducted in 2018, in which 1000 references based on the top Google Scholar search results were analyzed [16].”
• Text Mining in Big Data Analytics (2020, background): “[213] mined 1000 abstracts from the Google Scholar database for search results for technology infrastructure of solar forecasting, classified the concepts of solar forecasting on the full texts of 249 papers from Science Direct, and also undertook the keyword analysis and topic”