Empowering Indonesia’s energy transition planning through long-term energy system modelling: A technoeconomic dataset

As one of the most populous countries globally and a major emerging economy, Indonesia aims to significantly lower its carbon footprint and transform its power sector toward cleaner energy sources. To effectively plan and implement policies to achieve these goals, decision-makers require reliable, comprehensive data on Indonesia's energy systems. However, accessing energy data in Indonesia can be challenging, as is often the case in developing countries where data may be fragmented, out of date, or simply unavailable. In this regard, this data article paper presents an open-access dataset of Indonesia's energy systems to support energy modelling, policy analysis, and research, following the U4RIA goals of Ubuntu, Retrievability, Reusability, Repeatability, Reconstructability, Interoperability, and Auditability. The dataset encompasses historical and projected data from 2010 to 2070 on key aspects such as electricity demand by sector, electricity generation, installed capacity, capacity factors, costs of energy technologies, and renewable energy potentials for solar, wind, hydro, and geothermal power. The data has been collated from Indonesian government reports, international agencies, and academic literature and harmonised using documented assumptions. While Indonesia-specific, this dataset could serve as a template for developing similar energy datasets in other countries, which may face similar challenges in developing energy transition planning.

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Publication Details

Journal
Data in Brief
Published
2025-04-15
DOI
https://doi.org/10.1016/j.dib.2025.111563
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Empowering Indonesia’s energy transition planning through long-term energy system modelling: A technoeconomic dataset

Fernando Plazas-Niño, Mark Howells, Neve Fields, Raphael Slade et al.
Data in Brief
Integrated Energy Systems Optimization
article

Empowering Indonesia’s energy transition planning through long-term energy system modelling: A technoeconomic dataset

Fernando Plazas-Niño, Mark Howells, Neve Fields, Raphael Slade, Emma Richardson, Mutiara Ramadhani Nur Irbah
article en

Abstract

As one of the most populous countries globally and a major emerging economy, Indonesia aims to significantly lower its carbon footprint and transform its power sector toward cleaner energy sources. To effectively plan and implement policies to achieve these goals, decision-makers require reliable, comprehensive data on Indonesia's energy systems. However, accessing energy data in Indonesia can be challenging, as is often the case in developing countries where data may be fragmented, out of date, or simply unavailable. In this regard, this data article paper presents an open-access dataset of Indonesia's energy systems to support energy modelling, policy analysis, and research, following the U4RIA goals of Ubuntu, Retrievability, Reusability, Repeatability, Reconstructability, Interoperability, and Auditability. The dataset encompasses historical and projected data from 2010 to 2070 on key aspects such as electricity demand by sector, electricity generation, installed capacity, capacity factors, costs of energy technologies, and renewable energy potentials for solar, wind, hydro, and geothermal power. The data has been collated from Indonesian government reports, international agencies, and academic literature and harmonised using documented assumptions. While Indonesia-specific, this dataset could serve as a template for developing similar energy datasets in other countries, which may face similar challenges in developing energy transition planning.

Data in BriefVol. 60
Loughborough University (GB), Imperial College London (GB)
Affordable and clean energy
Openalex Percentile: Top 94%
Integrated Energy Systems Optimization
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Empowering Indonesia’s energy transition planning through long-term energy system modelling: A technoeconomic dataset — Fernando Plazas-Niño, Mark Howells, et al. · Data in Brief (2025) | TGRS Research Map | TGRS