@inproceedings {28389,
	title = {Data-Driven Pump Scheduling for Cost Minimization in Water Networks},
	journal = {2021 IEEE International Conference on Autonomous Systems (ICAS)2021 IEEE International Conference on Autonomous Systems (ICAS)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway.},
	month = {08/2021},
	publisher = {IEEE},
	address = {Montreal, QC, Canada},
	abstract = {Pumps consume a significant amount of energy in a water distribution network (WDN). With the emergence of dynamic energy cost, the pump scheduling as per user demand is a computationally challenging task. Computing the decision variables of pump scheduling relies over mixed integer optimization (MIO) formulations. However, MIO formulations are NP-hard in general and solving such problems is inefficient in terms of computation time and memory. Moreover, the computational complexity of solving such MIO formulations increases exponentially with the size of the WDN. As an alternative, we propose a data-driven approach to estimate the decision variables of pump scheduling using deep neural networks (DNN). We evaluate the performance of our trained DNN relative to a state-of-the-art MIO solver, and conclude that our DNN based approach can be used to minimize the pump switching and cost incurred due to dynamic energy in a given WDN with much lower complexity.},
	doi = {10.1109/ICAS49788.2021.9551168},
	url = {https://ieeexplore.ieee.org/document/9551168},
	author = {Bhardwaj, Jyotirmoy and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
