Proceedings of the 2nd International Conference on Nature-Based Solution in Climate Change, RESILIENCE 2023, 24 November 2023, Jakarta, Indonesia

Research Article

Modelling Solar Energy Suitability in Java Island Using Remote Sensing and Machine Learning

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  • @INPROCEEDINGS{10.4108/eai.24-11-2023.2346424,
        author={Ivani Christin Nugraheni and Anjar Dimara Sakti and Ketut  Wikantika},
        title={Modelling Solar Energy Suitability in Java Island Using Remote Sensing and Machine Learning},
        proceedings={Proceedings of the 2nd International Conference on Nature-Based Solution in Climate Change, RESILIENCE 2023, 24 November 2023, Jakarta, Indonesia},
        publisher={EAI},
        proceedings_a={RESILIENCE},
        year={2024},
        month={7},
        keywords={solar energy suitability remote sensing machine learning java island},
        doi={10.4108/eai.24-11-2023.2346424}
    }
    
  • Ivani Christin Nugraheni
    Anjar Dimara Sakti
    Ketut Wikantika
    Year: 2024
    Modelling Solar Energy Suitability in Java Island Using Remote Sensing and Machine Learning
    RESILIENCE
    EAI
    DOI: 10.4108/eai.24-11-2023.2346424
Ivani Christin Nugraheni1, Anjar Dimara Sakti1,*, Ketut Wikantika1
  • 1: Institut Teknologi Bandung
*Contact email: anjar@itb.ac.id

Abstract

The use of electrical energy on the island of Java in 2022 will be 45,835.45 MW. This shows an increase in energy use of 9.8% from 2021. Seeing the trend of energy demand which is increasing every year, the supply of electrical energy is also increasing. The increase in meeting energy needs has an impact on the energy crisis so alternatives are needed in the form of renewable energy, one of which is solar energy. This study will model and analyze the suitability of solar energy potential on the island of Java by integrating remote sensing data and machine learning methods. It is hoped that this study can be used as material for consideration in managing renewable resources on the island of Java. The results section shows that the machine learning method used shows the potential suitability of solar energy in medium to high class.