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Identifying Emerging Research Related to Solar Cells Field Using a Machine Leaning Approach

Original scientific paper

Journal of Sustainable Development of Energy, Water and Environment Systems
Volume 4, Issue 4, December 2016, pp 418-429
DOI: https://doi.org/10.13044/j.sdewes.2016.04.0032
Hajime Sasaki1 , Tadayoshi Hara2, Ichiro Sakata1,2
1 Policy Alternatives Research Institute, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
2 Innovation Policy Research Center, Institute of Engineering Innovation, School of Engineering, University of Tokyo, Yayoi 2-11-16, Bunkyo-ku, Tokyo, Japan


The number of research papers related to solar cells field is increasing rapidly. It is hard to grasp research trends and to identify emerging research issues because of exponential growth of publications, and the field’s subdivided knowledge structure. Machine learning techniques can be applied to the enormous amounts of data and subdivided research fields to identify emerging researches. This paper proposed a prediction model using a machine learning approach to identify emerging solar cells related academic research, i.e. papers that might be cited very frequently within three years. The proposed model performed well and stable. The model highlighted some articles published in 2015 that will be emerging in the future. Research related to vegetable-based dye-sensitized solar cells was identified as the one of the promising researches by the model. The proposed prediction model is useful to gain foresight into research trends in science and technology, facilitating decision-making processes.

Keywords: Solar cells, Photovoltaic, Emerging research, Technology prediction, Citation network, Machine learning, Scientometrics, Innovation management.

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