Optimizing Time Series Models for Water Demand Forecasting
This study focuses on optimizing time series forecasting models for water demand in a North Italian city as part of the Battle of the Water Demand Forecast (BWDF) challenge. It aims to accurately predict water demands across ten district-metered areas (DMAs) using historical data and weather informa...
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MDPI AG
2024-08-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/9 |
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| author | Gal Perelman Yaniv Romano Avi Ostfeld |
| author_facet | Gal Perelman Yaniv Romano Avi Ostfeld |
| author_sort | Gal Perelman |
| collection | DOAJ |
| description | This study focuses on optimizing time series forecasting models for water demand in a North Italian city as part of the Battle of the Water Demand Forecast (BWDF) challenge. It aims to accurately predict water demands across ten district-metered areas (DMAs) using historical data and weather information over a one-week horizon. The methodology encompasses data preprocessing, including missing data imputation, feature engineering, and novel normalization techniques, followed by the development and hyperparameter optimization of various data-driven models such as random forest, XGB, LSTM, and Prophet. Extensive cross-validation tests assess each model’s performance, revealing that our refined approach markedly enhances forecast accuracy, demonstrating the importance of model and parameter selection for effective water demand forecasting. |
| format | Article |
| id | doaj-art-4e99328d5ea74375953d1b9afadfc3ad |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-4e99328d5ea74375953d1b9afadfc3ad2025-08-20T02:42:42ZengMDPI AGEngineering Proceedings2673-45912024-08-01691910.3390/engproc2024069009Optimizing Time Series Models for Water Demand ForecastingGal Perelman0Yaniv Romano1Avi Ostfeld2Faculty of Civil and Environmental Engineering, Technion—Israel Institute of Technology, Haifa 32000, IsraelFaculty of Electrical Engineering and Computer Science, Technion—Israel Institute of Technology, Haifa 32000, IsraelFaculty of Civil and Environmental Engineering, Technion—Israel Institute of Technology, Haifa 32000, IsraelThis study focuses on optimizing time series forecasting models for water demand in a North Italian city as part of the Battle of the Water Demand Forecast (BWDF) challenge. It aims to accurately predict water demands across ten district-metered areas (DMAs) using historical data and weather information over a one-week horizon. The methodology encompasses data preprocessing, including missing data imputation, feature engineering, and novel normalization techniques, followed by the development and hyperparameter optimization of various data-driven models such as random forest, XGB, LSTM, and Prophet. Extensive cross-validation tests assess each model’s performance, revealing that our refined approach markedly enhances forecast accuracy, demonstrating the importance of model and parameter selection for effective water demand forecasting.https://www.mdpi.com/2673-4591/69/1/9time series forecastingwater demanddata-driven modelsdata normalization |
| spellingShingle | Gal Perelman Yaniv Romano Avi Ostfeld Optimizing Time Series Models for Water Demand Forecasting Engineering Proceedings time series forecasting water demand data-driven models data normalization |
| title | Optimizing Time Series Models for Water Demand Forecasting |
| title_full | Optimizing Time Series Models for Water Demand Forecasting |
| title_fullStr | Optimizing Time Series Models for Water Demand Forecasting |
| title_full_unstemmed | Optimizing Time Series Models for Water Demand Forecasting |
| title_short | Optimizing Time Series Models for Water Demand Forecasting |
| title_sort | optimizing time series models for water demand forecasting |
| topic | time series forecasting water demand data-driven models data normalization |
| url | https://www.mdpi.com/2673-4591/69/1/9 |
| work_keys_str_mv | AT galperelman optimizingtimeseriesmodelsforwaterdemandforecasting AT yanivromano optimizingtimeseriesmodelsforwaterdemandforecasting AT aviostfeld optimizingtimeseriesmodelsforwaterdemandforecasting |