A Study on Short-Term Water-Demand Forecasting Using Statistical Techniques
This paper proposes a method for short-term weekly water-demand forecasting combining various statistical techniques. In the proposed method, training datasets are prepared through exploratory data analysis, several data preprocessing steps, and an input selection step; also, forecasting models are...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/69/1/154 |
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| Summary: | This paper proposes a method for short-term weekly water-demand forecasting combining various statistical techniques. In the proposed method, training datasets are prepared through exploratory data analysis, several data preprocessing steps, and an input selection step; also, forecasting models are constructed by support vector regression. After this, weekly water-demand forecasts are calculated using iterated and direct strategies. To verify the performance, the proposed method is applied to urban hourly water-demand datasets provided by the Battle of Water Demand Forecasting organized in the 3rd WDSA-CCWI Joint Conference. |
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| ISSN: | 2673-4591 |