Prediction of natural gas consumption in residential and commercial buildings; a case of 5 big cities located in the United States

Abstract

Abstract The aims of this study is to predict natural gas consumption and price in residential and commercial building into five big cities (California, Washington, New-York, Texas, and Florida) in the United States. Three different machine learning algorithms such as linear model (LM), Support Vector Machine regression (SVMR), and Random Forest regression (RFR) have been used. Four statistical tests (ANOVA, Chi-square, regression, and Minitab tests) have allowed to select among the eleven weather parameters those that affected significantly the performance of natural gas. Finally, after applied these four tests, only minimum, mean, maximum air temperature and mean air speed have been recognized as principal parameters having a direct impact on the natural gas consumption. To decide on the success of these algorithms, four different statistical metrics (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Regression coefficient R², and mean square error (MSE)) were discussed in this study. The results showed that linear model and Random forest regression could be applied to predict the natural gas consumption with a good accuracy, despite this, Random Forest regression model is the best fitting model among all the three models used. It is followed by LMR, and SVMR, respectively.