Improving the Performance of Water Demand Forecasting Models by Using Weather Input
| Autor: | Mark Bakker, H. Van Duist, J. H. G. Vreeburg, Luuk C. Rietveld, K. M. van Schagen |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Meteorology
Ensemble forecasting MLR model Computer science business.industry Heuristic Water supply General Medicine Demand forecasting Short term Sociology of Development and Change Environmental Technology Anomaly detection Milieutechnologie Probabilistic forecasting Performance improvement Transfer/-noise model Sociologie van Ontwikkeling en Verandering business North American Mesoscale Model Physics::Atmospheric and Oceanic Physics Weather input Engineering(all) |
| Zdroj: | Procedia Engineering, 70, 93-102 Procedia Engineering, 70, 2014; CCWI 2013: 12th International Conference on Computing and Control for the Water Industry Procedia Engineering 70 (2014) |
| ISSN: | 1877-7058 |
| DOI: | 10.1016/j.proeng.2014.02.012 |
| Popis: | Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptive Heuristic model, a Transfer/-noise model, and a Multiple Linear Regression model. The performance of the models was studied both with and without using weather input, in order to assess the possible performance improvement due to using weather input. Simulations with the models showed that when using weather input the largest forecasting errors can be reduced by 11%, and the average errors by 7%. This reduction is important for the application of the forecasting model for the control of water supply systems and for anomaly detection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|