Gas consumption prediction based on artificial neural networks for residential sectors

Abstract

The objective of this work is to improve gas supply efficiency in residential districts. To achieve this goal, Artificial Neural Networks (ANNs) have been used. In this work, a hybrid model based on ANN has been proposed that obtains total daily gas consumption (in KWh) in residential districts, with a prediction horizon of 7 days. Previous consumption records and meteorological variables have been considered to improve the prediction of future gas consumption. In order to find the best ANN that models the behavior of this consumption variable, a set of experiments has been designed, where the mean square error of each network is measured to rate their reliability and accuracy. A hybrid neural model has been created to determine a horizon of 7 predictions using a median filter of the 5 best predictors per day.