Week-Ahead Water Demand Forecasting Using Convolutional Neural Network on Multi-Channel Wavelet Scalogram
Year: 2024
Type: conference-paper
Abstract: Water management is vital for building an adaptive and resilient society. Water demand forecasting aids water management by learning the underlying relationship between consumption and governing variables for optimal supply. In this paper, we propose a week-ahead hourly water demand forecasting technique based on deep learning (DL) utilizing an encoded representation of historical supply data and influencing exogenous variables for a District Metered Area (DMA). We deploy a CNN model with and without attention and evaluate the model’s ability to forecast the supply for different DMAs with varying characteristics. The performances are quantitatively and qualitatively compared against a baseline LSTM.
Authors: Adithya Ramachandran, Hatem Mousa, Andreas Maier, Siming Bayer
Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402999280