A new approach for developing a hybrid sun-tracking method of the intelligent photovoltaic blinds considering the weather condition using data mining technique

Hyuna Kang, Taehoon Hong, Minhyun Lee

Research output: Contribution to journalArticle

Abstract

As a part of technology innovation in the building sector, an intelligent photovoltaic blind (i-PB) with direct and indirect sun-tracking methods were previously developed by this research team. Due to the shadows on the tightly aligned slats of the i-PB, however, there is a difference in the electricity according to the weather and sun-tracking method. Accordingly, this study aimed to develop a hybrid sun-tracking method of the i-PB, which can determine the sun-tracking method with highest electricity generation between the two sun-tracking methods according to the weather. To this end, this study proposed a new approach for developing a hybrid sun-tracking method by selecting the main climate factors and their threshold using data mining technique. As a result of the experimental study conducted in South Korea, a hybrid sun-tracking method in autumn was developed. To ensure the effectiveness of the new approach, a real-time sun-tracking system was developed and used for the experimental validation. As a result, the hybrid sun-tracking method showed the highest electricity generation (i.e., 97.3 Wh/m2) among the three sun-tracking methods, and 84.9% prediction accuracy. The proposed approach can provide a more comprehensive solution by maximizing the advantages of each sun-tracking method and minimizing its weaknesses.

Original languageEnglish
Article number109708
JournalEnergy and Buildings
Volume209
DOIs
Publication statusPublished - 2020 Feb 15

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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