Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm

Effectively managing and optimizing energy resources to accommodate population growth while minimizing carbon emissions has become increasingly intricate. A proficient approach to this dilemma is accurately predicting energy usage and emissions across diverse sectors. This paper unveils a genetic algorithm (GA)-optimized support vector regression (SVR) model designed to (i) predict electricity generation, (ii) predict energy consumption in four primary sectors-residential, industrial, commercial, and agricultural, and (iii) estimate sector-specific carbon emissions. The proposed model's efficacy is assessed by calculating the R² value, mean absolute error (MAE), root mean squared error (RMSE), and residual plot. The model achieved high accuracy in predicting energy generation, with an MAE of 1.18 %, and yielded reliable sectoral consumption predictions, reflected in MAE values of 1.22 % (residential), 4.98 % (industrial), 4.40 % (commercial), and 4.04 % (agricultural). The residuals exhibited homoscedasticity, and the R² value approached one. The model predicts that by 2027, the residential sector will consume 55748.66 GWh of energy, the commercial sector 14892.49 GWh, the industrial sector 32642.35 GWh, and the agricultural sector 2288.37 GWh. It has been predicted that by 2027, these four sectors will release 75437.96-billion-gram carbon equivalents.