Optimal planning of a rooftop PV system using GIS-based reinforcement learning

Seunghoon Jung, Jaewon Jeoung, Hyuna Kang, Taehoon Hong

Research output: Contribution to journalArticlepeer-review

Abstract

This study aimed to develop a geographic information system (GIS)-based reinforcement learning (RL) model for optimal planning of a rooftop PV system, considering the uncertainty of future scenarios throughout the life cycle of buildings. To that end, GIS was used to establish the spatial data for the rooftop PV installation, and an RL model was developed to maximize the economic profit of the rooftop PV installation in various locations and future scenarios. The developed model was applied to residential buildings in Nonhyeon district, South Korea to evaluate their economic profitability and to compare the model with the existing planning methods. With the use of the developed GIS-based RL model, the rooftop PV system became economically feasible, achieving average economic profit of 539,197 USD over all scenarios for all target buildings which was higher than that of the existing models by 4.4% and 4.3%. Furthermore, the developed model outperformed the existing models especially in volatile scenarios with lower solar radiation. Therefore, the use of the proposed GIS-based RL model can optimize the economic feasibility of rooftop PV systems for buildings, which will benefit building owners and community-level energy business owners. In conclusion, the developed model can promote the adoption of rooftop PV systems, which have 91.8% lower global warming potential than the Korean mixed grid, without additional subsidies to achieve Korea's national CO2 emission reduction plan.

Original languageEnglish
Article number117239
JournalApplied Energy
Volume298
DOIs
Publication statusPublished - 2021 Sep 15

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT; Ministry of Science and ICT) (NRF-2018R1A5A1025137).

Publisher Copyright:
© 2021 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Mechanical Engineering
  • Energy(all)
  • Management, Monitoring, Policy and Law

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