Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series
Short-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 min time steps, which improves the forecasting accuracy b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/12/6/1683 |
| _version_ | 1797565497903939584 |
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| author | Shan Wu Hongquan Han Benwei Hou Kegong Diao |
| author_facet | Shan Wu Hongquan Han Benwei Hou Kegong Diao |
| author_sort | Shan Wu |
| collection | DOAJ |
| description | Short-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 min time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases. |
| first_indexed | 2024-03-10T19:14:01Z |
| format | Article |
| id | doaj.art-6a9c34a54f114b44b3775fb1381d1ab4 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T19:14:01Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-6a9c34a54f114b44b3775fb1381d1ab42023-11-20T03:35:43ZengMDPI AGWater2073-44412020-06-01126168310.3390/w12061683Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time SeriesShan Wu0Hongquan Han1Benwei Hou2Kegong Diao3College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, ChinaCollege of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, ChinaCollege of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, ChinaFaculty of Computing, Engineering, and Media, De Montfort University, The Gateway, Leicester LE1 9BH, UKShort-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 min time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases.https://www.mdpi.com/2073-4441/12/6/1683water demand forecastinghybrid modelerror correctionchaotic time seriesleast square support vector machine |
| spellingShingle | Shan Wu Hongquan Han Benwei Hou Kegong Diao Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series Water water demand forecasting hybrid model error correction chaotic time series least square support vector machine |
| title | Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series |
| title_full | Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series |
| title_fullStr | Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series |
| title_full_unstemmed | Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series |
| title_short | Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series |
| title_sort | hybrid model for short term water demand forecasting based on error correction using chaotic time series |
| topic | water demand forecasting hybrid model error correction chaotic time series least square support vector machine |
| url | https://www.mdpi.com/2073-4441/12/6/1683 |
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