Forecasting Residential Natural Gas Consumption in Tehran Using Machine Learning Methods, M.Sc. Thesis Sharif University of Technology ; Maleki, Abbas (Supervisor)
Abstract
According to increasing energy demand in Iran and the world, the role of natural gas as a relatively clean and cost-effective source has received more attention. Given the high share of the residential sector in the country's natural gas consumption, providing a model for forecasting the demand of this sector is of great importance for policy makers and decision makers in this field. In the present study, we employ three popular methods of machine learning, support vector regression, artificial neural network and decision tree to predict the consumption of natural gas in the residential sector in Tehran according to meteorological parameters (including temperature, precipitation and wind...
Cataloging briefForecasting Residential Natural Gas Consumption in Tehran Using Machine Learning Methods, M.Sc. Thesis Sharif University of Technology ; Maleki, Abbas (Supervisor)
Abstract
According to increasing energy demand in Iran and the world, the role of natural gas as a relatively clean and cost-effective source has received more attention. Given the high share of the residential sector in the country's natural gas consumption, providing a model for forecasting the demand of this sector is of great importance for policy makers and decision makers in this field. In the present study, we employ three popular methods of machine learning, support vector regression, artificial neural network and decision tree to predict the consumption of natural gas in the residential sector in Tehran according to meteorological parameters (including temperature, precipitation and wind...
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