@article{RING2025_Martin,

title = {Metalearning for improving time series forecasting based on deep learning: A water case study},

author = {Martín Solís and A. Gil-Gamboa and Alicia Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S2590123025035960},

doi = {10.1016/j.rineng.2025.107541},

year  = {2025},

date = {2025-12-09},

urldate = {2025-12-09},

journal = {Results in Engineering},

volume = {28},

pages = {107541},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{GIL-GAMBOA25,

title = {Energy-efficient transfer learning for water consumption forecasting},

author = {A. Gil-Gamboa and J. F. Torres and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S2210537925000502},

doi = {https://doi.org/10.1016/j.suscom.2025.101130},

year  = {2025},

date = {2025-05-07},

urldate = {2025-05-07},

journal = {Sustainable Computing: Informatics and Systems},

volume = {46},

pages = {101130},

abstract = {Artificial intelligence is expanding at an unprecedented rate due to the numerous advantages it provides to all types of businesses and industries. Water utilities are adopting artificial intelligence models to optimize water management in cities nowadays. However, the substantial computational demands of artificial intelligence present challenges, particularly regarding energy consumption and environmental impact. This paper addresses this problem by proposing a transfer learning approach for water consumption forecasting that reduces computational time, energy usage, and CO$_2$ emissions. The proposed methodology consists in developing a transfer learning approach based on a deep learning model already trained for a task with similar characteristics such as predicting electricity consumption. Thus, a pre-trained deep learning model designed for electricity consumption prediction is adapted to the water consumption domain, leveraging shared characteristics between these tasks. Experiments are conducted to determine the optimal amount of knowledge transfer and compare the performance of this approach with other state-of-the-art time-series forecasting models. Using real data from a water company in Spain, the transfer learning model achieves a similar or better accuracy than the other methods, while demonstrating significantly lower computational times, energy consumption and CO2 emissions. In addition, a scalability analysis has been conducted leading to the conclusion that the proposed transfer learning model is highly suitable to deal with big data. These findings highlight the potential of transfer learning as a sustainable and scalable solution for big data challenges in water management systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ESWA2024,

title = {Medium-term water consumption forecasting based on deep neural networks},

author = {A. Gil-Gamboa and P. Paneque and O. Trull and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S095741742400099X},

doi = {10.1016/j.eswa.2024.123234},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Expert Systems With Applications},

volume = {247},

pages = {123234},

abstract = {Water consumption forecasting is an essential tool for water management, as it allows for efficient planning and allocation of water resources, an undervalued but indispensable resource for all living beings. With the increasing demand for accurate and timely water forecasting, traditional forecasting methods are proving to be insufficient. Deep learning techniques, which have shown remarkable performance in a wide range of applications, offer a promising approach to address the challenges of water consumption forecasting. In this work, the use of deep learning models for medium-term water consumption forecasting of residential areas is explored. A deep feed-forward neural network is developed to predict water consumption of a company’s customers for the next quarter. First, customers are grouped according to their consumption as these customers include both household consumers and special consumers such as public swimming pools, sports halls or small industries. Then, a deep feed-forward neural network is designed for household customers by obtaining the optimal values for those hyperparameters that have a great influence on the network performance. Results are reported using a real-world dataset composed of the water consumption from 1999 to 2015 on a quarterly basis, corresponding to 3262 clients of a water supply company. Finally, the proposed algorithm is evaluated by comparing it with other reference algorithms including an LSTM network.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}