Energy consumption forecasting in agriculture by artificial intelligence and mathematical models

Abstract

Energy management and reduction of CO2 emission lead to many investigates about energy input–output analyses especially in agricultural sector. The main objectives of this study are to assess the energy use pattern and to select the best method among Cobb–Douglas (CD), multiple linear regressions (MLR), multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM) models to estimate potato output energy in Jiroft city, located in the south of Kerman province, Iran. Data were collected with questioner method from expert farmers. Results indicated that the average of total input energy is about 84309.43 MJ ha−1 and the average of total output energy is 130217.14 MJ ha−1. Irrigation water (36%) and fertilizers (26%) were found to be the most important energy inputs in potato production. Unlike most literature reviews, in this study for better and more accurate model evolutions in energy forecasting, five different sizes of training selection (TS) were used: 50%, 60%, 70%, 80% and 90%. Some statistical indexes (RMSE, MAPE, and R2) of the different data selection calculated from k-fold in two training sets. The results showed that RBF model has a great prediction performance at all different values of training data selection. The average value of R2 was found to be more than 0.98. Between SVM and MLP models, the test performance will be improved by size reduction of training selection. Thus, the RBF model is chosen as the best model for fitting and modeling the output energy of potato production.