Modeling and Forecasting of Energy Consumption in Food and Processing Industry using Artificial Neural Networks

In all societies and countries, in order to plan to provide the required energy for various sectors, it is necessary to accurately predict the demand, type of energy carriers and energy supply method. Considering the importance of food industries in each country, in this study, modeling of required energy for food industries sector was investigated. Modeling of energy consumption was performed using artificial neural networks. In the first step, the input data to the model was calculated according to statistics, balance sheets and input method proposed in this paper. Two methods, namely multiple neural network and single neural network were tested and the results showed that multiple neural network has a higher accuracy. For each of the energy carriers (gasoline, kerosene, fuel oil, natural gas, electricity, gasoline and LPG) the best neural network was selected by taking the average of 20 times per program for each network characteristic. Finally, the network was implemented in the form of final model using Simulink environment of MATLAB 7.0 software. Data analysis showed that daily consumption of natural gas in the industry is increasing, while the consumption of fuel oil and LPG is going to be decreased.