Prediction of Renewable Energy Potential to Prevent Greenflation Using Bayesian Structural Time Series: BSTS with JASP Software to Predict PLTMH Potential
DOI:
https://doi.org/10.70610/iare.v4i1.1101Keywords:
Bayesian Structural Time Series, Energy Policy, Greenflation, Micro-Hydropower, Renewable EnergyAbstract
The global transition toward renewable energy is increasingly urgent to mitigate climate change and reduce dependence on fossil fuels; however, it also introduces economic risks such as greenflation, driven by rising demand for green commodities. In Indonesia, renewable energy development has become a national priority, with Jambi Province identified as a strategic region due to its significant micro-hydropower (PLTMH) potential. This study aims to predict PLTMH potential as a means of supporting energy transition planning and preventing greenflation through data-driven policy decisions. The research employs a Bayesian Structural Time Series (BSTS) approach using JASP software, integrating Kalman Filter, spike-and-slab regression, and Bayesian Model Averaging. Time series data from 2008–2024 were analyzed with 2,000 MCMC draws and a 1% burn-in to ensure estimation stability. The results demonstrate a strong upward trend in PLTMH capacity, with high model accuracy indicated by an R² value of 0.991, low residual standard deviation, and acceptable prediction uncertainty. Forecasts suggest continued growth in PLTMH capacity over the next two decades before reaching a steady state. The study concludes that BSTS is a robust and reliable method for predicting renewable energy potential and supporting counterfactual policy analysis. This research contributes empirically to applied Bayesian time series modeling and practically to renewable energy policy planning, offering evidence-based insights to enhance energy security and mitigate greenflation risks.
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