MULTI OPTIMAL DESIGN OF GMDH TYPE-NN FOR MODELING AND PREDICTION OF GAS CONSUMPTION IN RASHT CITY

It is widely accepted that natural gas is a clean energy source that can be used to meet energy demand for heating and industrial purpose among the fossil fuels and its usage remarkably increases in order to maintain a clean environment in many countries in the world. Therefore, energy demand for various sectors should be estimated in the frame of short-term energy policy. In this paper, multi-objective evolutionary pareto optimal design of GMDH type-Neural Network has been used for modeling and predicting of gas consumption in Rasht, Guilan, Iran, using input-output data sets. In this way, multi-objective uniform-diversity genetic algorithms (MUGA) are then used for pareto optimization of GMDH networks. Input data set (mean temperature, moisture, rainfall and number of units) were obtained from the regional gas distribution company and the local meteorology office in last 7 years. The predicted values were compared with those of experimental values in order to estimate the performance of the GMDH network

Keywords:

GMDH , Natural gas consumption , Multi-objective uniform-diversity genetic algorithms (MUGA) , Forecasting methodology , Neural Networks