Issue |
E3S Web Conf.
Volume 629, 2025
2025 15th International Conference on Future Environment and Energy (ICFEE 2025)
|
|
---|---|---|
Article Number | 05005 | |
Number of page(s) | 10 | |
Section | Renewable Energy Technologies and System Optimization | |
DOI | https://doi.org/10.1051/e3sconf/202562905005 | |
Published online | 05 June 2025 |
A Geoanalytical and AI-Driven Approach for Optimising Biomass Energy Generation from Key Economic Crops in Northeastern Thailand
1 Department of Agricultural Engineering, Khon Kaen University, Thailand.
2 Department of Environmental Engineering, Khon Kaen University, Thailand.
3 Department of Computer Engineering, Khon Kaen University, Thailand.
* Corresponding author: kulyakorn@kku.ac.th
The transition toward renewable energy sources has become a global priority to address climate change and energy security concerns. Biomass energy, derived from agricultural crops and residues, has emerged as a promising alternative to fossil fuels due to its sustainability and potential to reduce greenhouse gas emissions. This research study presents the development of a a digital platform for biomass resource management for sugarcane and other economic crops, including rice, maize, oil palm, and cassava, in Northeastern Thailand to support strategic planning for biomass energy generation. The system is designed to optimise the value chain by analysing the cultivation potential of these five crops and geoanalytics within a 50-kilometer radius around biomass power plants, this research analyses the potential of each biomass crop at three levels—high, medium, and low—reflecting the feasibility of converting agricultural residues into energy. Historical data over the past five years were utilised to assess the potential energy output from these crops. Furthermore, artificial intelligence (AI) technologies were employed to forecast key sugarcane parameters, including cane yield, cane cultivation area, and production output. The research involved evaluating 18 AI models, comparing their performance using metrics such as Mean Absolute Error (MAE), Coefficient of Determination (R2), and Root Mean Square Error (RMSE), to identify the most accurate model for long-term forecasting. A 10-year prediction was conducted to provide actionable insights. The system serves as a valuable tool for government agencies to enhance and promote policies that leverage Northeastern Thailand’s key economic crops as sustainable biomass resources, contributing to clean energy generation and added economic value.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.