Water Demand Forecasting of District Metered Areas through Learned Consumer Representations

Ramachandran A, Flensmark B. Neergaard T, Arias Vergara T, Maier A, Bayer S (2025)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2025

Conference Proceedings Title: Water Demand Forecasting of District Metered Areas through Learned Consumer Representations

Event location: Isola delle Femmine, Palermo, Italy

URI: https://eusipco2025.org/wp-content/uploads/pdfs/0001842.pdf

Abstract

Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate change, securing water resources and supply has emerged as an urgent global issue with extensive socioeconomic ramifications. Hourly consumption data from end-users have yielded substantial insights for projecting demand across regions characterized by diverse consumption patterns. Nevertheless, the prediction of water demand remains challenging due to influencing non-deterministic factors, such as meteorological conditions. This work introduces a novel method for short-term water demand forecasting for District Metered Areas (DMAs) which encompass commercial, agricultural, and residential consumers. Unsupervised contrastive learning is applied to categorize end-users according to distinct consumption behaviors present within a DMA. Subsequently, the distinct consumption behaviors are utilized as features in the ensuing demand forecasting task using wavelet-transformed convolutional networks that incorporate a cross-attention mechanism combining both historical data and the derived representations. The proposed approach is evaluated on real-world DMAs over a six-month period, demonstrating improved forecasting performance in terms of MAPE across different DMAs, with a maximum improvement of 4.9%. Additionally, it identifies consumers whose behavior is shaped by socioeconomic factors, enhancing prior knowledge about the deterministic patterns that influence demand.

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How to cite

APA:

Ramachandran, A., Flensmark B. Neergaard, T., Arias Vergara, T., Maier, A., & Bayer, S. (2025). Water Demand Forecasting of District Metered Areas through Learned Consumer Representations. In Water Demand Forecasting of District Metered Areas through Learned Consumer Representations. Isola delle Femmine, Palermo, Italy, IT.

MLA:

Ramachandran, Adithya, et al. "Water Demand Forecasting of District Metered Areas through Learned Consumer Representations." Proceedings of the European Signal Processing Conference - EUSIPCO 2025, Isola delle Femmine, Palermo, Italy 2025.

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