Ramachandran A, Mousa H, Maier A, Bayer S (2024)
Publication Language: English
Publication Type: Journal article
Publication year: 2024
Book Volume: 69
Article Number: 179
Journal Issue: 1
DOI: 10.3390/engproc2024069179
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.
APA:
Ramachandran, A., Mousa, H., Maier, A., & Bayer, S. (2024). Week-Ahead Water Demand Forecasting Using Convolutional Neural Network on Multi-Channel Wavelet Scalogram †. Engineering Proceedings, 69(1). https://doi.org/10.3390/engproc2024069179
MLA:
Ramachandran, Adithya, et al. "Week-Ahead Water Demand Forecasting Using Convolutional Neural Network on Multi-Channel Wavelet Scalogram †." Engineering Proceedings 69.1 (2024).
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