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Conference Paper
A Study on Short-Term Water-Demand Forecasting Using Statistical Techniques
- Authors
- Jungwon Yu, Hyansu Bae, Mi-Seon Kang, Kwang-Ju Kim, In-Su Jang
- Issue Date
- 2024-07
- Citation
- International Joint Conference of Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI) 2024, pp.1-5
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Data Preprocessing, Exploratory Data Analysis, Preprocessing Steps, Statistical techniques, forecasting model, input selection, short-term, support vector regression(SVR), water demand forecasting
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
