Artificial Neural Network Model to Forecast Energy Consumption in Wheat Production in India

Abstract

Abstract Energy analysis in agriculture sector require modelling technique that can incorporate complex unknown interactions and non-linearity in systems. In this study Artificial neural network technique is used to model and forecast input energy consumed in wheat production in India and is compared for predictive accuracy with linear models. Household data from 256 farmers revealed that the average input energy consumed in region is 29612.43 MJ/ha with urea (47%), diesel (31.5%) and electricity (9.8%) being three main contributors. Multi-layered feed forward model with 2 hidden layers with 8 and 15 neurons respectively and sigmoidal activation function in hidden layers and output layers under gradient descent training algorithm gave the best results. The R2 was 0.99 for training dataset and 0.973 for validation data set, while for MLR model it was 0.95 and 0.73 for respective datasets. The root mean squared error (RMSE) in ANN model was 4779.2 MJ/ha and 2008.96 MJ/ha for training and validation data, respectively. This prediction system could forecast input energy with error margin of ± 7889.83 MJ/ha on training dataset and ± 3298.47 MJ/ha on validation data under various combinations. Sensitivity analysis showed that urea, diesel, and electricity are the important factors in input energy forecasting.

Keywords

• Neural network
• Input energy
• MLR
• Sensitivity analysis
• Wheat
• India