Forecasting the Supply Chain Demand of Agricultural Residues for Biomass Power Plants Using Machine Learning
Keywords:
biomass, agricultural residues, forecasting, machine learningAbstract
This research investigated the supply chain of biomass derived from agricultural residues of five crop types in the Northern region of 17 provinces in Thailand, with the aim of evaluating the potential quantity of agricultural residues available for electricity generation through biomass. Historical data was utilized, and 5 machine learning models were employed to determine the optimal model for the dataset, which included 11 factors. The dataset was divided into 80% for training the models and 20% for testing. The Random Forest Regression model with n_neighbors = 1 emerged as the most effective for predicting production from this dataset, achieving a Mean Absolute Percentage Error (MAPE) of 27.511%. The results indicate that machine learning techniques can be utilized to forecast agricultural production effectively. This approach enables the estimation of agricultural residues from various crops, thus facilitating the calculation of biomass energy derived from agricultural residues for biomass power generation. The implications of these findings are substantial, as they demonstrate the potential of machine learning in the utilization of biomass resources for renewable energy production.
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