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.
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Acknowledgement
This work comes under the framework of the project IT874-13 granted by the Basque Regional Government. The authors would like to thank the company EDP-Naturgas that has given permission to use in this work the data collected from two communities.
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Porto, A., Irigoyen, E. (2018). Gas Consumption Prediction Based on Artificial Neural Networks for Residential Sectors. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_10
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DOI: https://doi.org/10.1007/978-3-319-67180-2_10
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