Neural Network-Informed Optimal Water Flow Problem: Modeling, Algorithm, and Benchmarking

This dataset accompanies the article “Neural Network-Informed Optimal Water Flow Problem: Modeling, Algorithm, and Benchmarking” and is intended to support full reproducibility of the numerical experiments. It provides all data needed to (i) construct the two benchmark water distribution networks (NET1 and Anytown), (ii) formulate the associated Optimal Water Flow (OWF) problems, and (iii) train the ICNN and IC2N surrogate models used to approximate pipe friction losses and pump energy consumption. The repository is organized so that researchers can directly reuse the network inputs and training datasets to implement, validate, and extend the proposed neural network-informed optimization framework.

The repository is organized into two main folders, NET1 and Anytown, each corresponding to one case study. Within each of these folders, the Water_network_input subdirectory contains all data needed to build the water network and formulate the OWF problem (including topology, hydraulic parameters, demand profiles, and electricity price profiles), while the Training_data subdirectory contains the supervised learning datasets used to train the neural networks: ICNN for pipe friction losses and IC2N for pump energy consumption.