Short-Term Urban Water Demand Forecasting Using an Improved NeuralProphet Model
The use of machine learning models for short-term network flow prediction has become increasingly widespread in recent years. Existing data-driven models are usually able to achieve good accuracy, but machine learning models are usually weakly interpretable and cannot provide clear decision guidance...
Saved in:
| Main Authors: | Yao Yao, Haixing Liu, Fengrui Gao, Hongcai Guo, Jiaxuan Zou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/69/1/175 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data
by: Necati Aksoy, et al.
Published: (2025-01-01) -
Neural Prophet driven day-ahead forecast of global horizontal irradiance for efficient micro-grid management
by: Stephen Oko Gyan Torto, et al.
Published: (2024-12-01) -
Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model
by: Shuang Zeng, et al.
Published: (2025-01-01) -
Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions
by: Carlos Alejandro Perez Garcia, et al.
Published: (2025-07-01) -
Optimizing Short-Term Water Demand Forecasting: A Comparative Approach to the Battle of Water Demand Forecasting
by: Bruno Ferreira, et al.
Published: (2024-09-01)