Thailand is an agricultural country. Biomass resources, especially some agricultural residues, are abundant in the country. This article studied predicting the agricultural residues of Thailand by multiple linear regression for five main agricultural products with high residue energy potential, namely, sugarcane, paddy, oil palm, cassava, and maize, planted in rural areas. Multiple linear regression models are applied to predicting the long-term biomass energy from agricultural residues in Thailand. The inputs of models are number of population, gross domestic product, planted area, annual rain fall, and mean temperature, which are used as inputs in the model to predict quantity of agricultural residues for biomass energy. The results from individual regression testing show that the number of population and planted area has an insignificant effect on agricultural residues. A linear regression analysis indicates that only gross domestic product, annual rain fall, and mean temperature affect the agricultural residues significantly. The correlation is y(AR) = 209: 4293 vertical bar 0. 00213x(GDP) 0.01235x(RAIN) 6.77107x(TEMP). The coefficient of determination (r(2)) of the regression model is 0.93.