In this paper, a logistic based approach is used to forecast the natural gas consumption for residential as well as commercial sectors in Iran. This approach is relatively simple compared with other forecasting approaches. To make this approach even simpler, two different methods are proposed to estimate the logistic parameters. The first method is based on the concept of the nonlinear programming (NLP) and the second one is based on genetic algorithm (GA). The forecast implemented in this paper is based on yearly and seasonal consumptions. In some unusual situations, such as abnormal temperature changes, the forecasting error is as high as 8.76%. Although this error might seem high, one does not need to be deeply concerned about the overall results since these unusual situations could be filtered out to yield more reliable predictions. In general, the overall results obtained using NLP and GA approaches are as well as or even in some cases better than the results obtained using some older approaches such as Cavallini's. These two approaches along with the gas consumption data in Iran for the previous 10 years are used to predict the consumption for the 11th, 12th, and 13th years. It is shown that the logistic approach with the use of NLP and GA yields very promising results.Natural gas consumption prediction Natural gas consumption forecast Logistic equation Nonlinear programming Genetic algorithm
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