Evaluation of the Performance Gains in Short-Term Water Consumption Forecasting by Feature Engineering via a Fuzzy Clustering Algorithm in the Context of Data Scarcity-Reference-Cited by-同舟云学术

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Evaluation of the Performance Gains in Short-Term Water Consumption Forecasting by Feature Engineering via a Fuzzy Clustering Algorithm in the Context of Data Scarcity

Published:2023-08-28 Issue: Volume: Page:
ISSN:
Container-title:16th International Conference on Meteorology, Climatology and Atmospheric Physics—COMECAP 2023
language:
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Author:

Tzanes Georgios¹²^ORCID,Papapostolou Christiana²,Gymnopoulos Miltiadis¹,Kaldellis John²,Stamou Anastasios¹

Affiliation:

1. Laboratory of Applied Hydraulics, Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Heroon Polytechniou 5, 15780 Athens, Greece

2. Soft Energy Applications & Environmental Protection Laboratory, Mechanical Engineering Department, School of Engineering, University of West Attica, 250 Thivon & Petrou Ralli, 12244 Athens, Greece

Publisher

MDPI

Link

https://www.mdpi.com/2673-4931/26/1/105/pdf

Reference9 articles.

1. ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands;Sarmas;Sustain. Cities Soc.,2022

2. Short term water demand forecast modelling using artificial intelligence for smart water management;Kavya;Sustain. Cities Soc.,2023

3. Tukey, J.W. (2008). The Concise Encyclopedia of Statistics, Springer.

4. Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Hardt, M., Recht, B., and Talwalkar, A. (2018). A System for Massively Parallel Hyperparameter Tuning. arXiv.

5. (2023, June 01). JDWarner/scikit-fuzzy: Scikit-Fuzzy Version 0.4.2. Available online: https://zenodo.org/record/3541386.

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