The Usage of Machine Learning for Policy Projection of Bio Solar Consumption in Indonesia
DOI:
https://doi.org/10.18196/jsp.v15i1.360Keywords:
Machine Learning, Policy Projection, Bio Solar Consumption, IndonesiaAbstract
This paper explores the application of machine learning (ML) techniques to project the future consumption of bio solar energy in Indonesia, aiming to inform and guide policy decisions in the energy sector. The transition to renewable energy sources is crucial for sustainable development, especially in emerging economies like Indonesia, which has shown a growing interest in bio solar energy. This research method uses Quantitative Research with Linear Regression and Sarima approaches. We employed several ML models, using Phyton which analyze with Multiple Linear Regression, Lasso Regression and Sarima, to analyze historical data on energy consumption, economic
indicators, demographic changes, and technological advancements. Our findings indicate that machine learning models can effectively predict bio solar consumption trends, highlighting the influence of economic growth, urbanization, and technological innovation on renewable energy adoption. The models suggest an increasing trajectory in bio solar consumption, driven by policy incentives,
technological advancements, and a growing awareness of environmental issues. The accuracy of ml predictions is contingent upon the availability and quality of data. Furthermore, the projections may not account for unforeseen economic or technological changes. Future research should focus on incorporating more dynamic data sources and exploring the impact of policy changes on renewable energy adoption. In conclusion, leveraging machine learning for policy projection offers a promising approach to support the growth of bio solar consumption in Indonesia. This study provides a foundation for future research and highlights the potential of ml in crafting informed, effective energy policies.
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