Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting
This work proposes a cascade model incorporating Long–Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which offers a more reliable model to forecast short-term (hourly) and medium horizon (week) water demand. The MLP model integrates the previously forecasted demand with exogenous variabl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/42 |
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| author | Bruno Brentan Ariele Zanfei Martin Oberascher Robert Sitzenfrei Joaquin Izquierdo Andrea Menapace |
| author_facet | Bruno Brentan Ariele Zanfei Martin Oberascher Robert Sitzenfrei Joaquin Izquierdo Andrea Menapace |
| author_sort | Bruno Brentan |
| collection | DOAJ |
| description | This work proposes a cascade model incorporating Long–Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which offers a more reliable model to forecast short-term (hourly) and medium horizon (week) water demand. The MLP model integrates the previously forecasted demand with exogenous variables, functioning as a filter to enhance the accuracy of the LSTM estimation. The LSTM model estimates, utilizing a univariate approach, the hourly forecasting of water demand for the entire available dataset and the minimum night flow. The algorithm considers various time series sizes for each DMA and predicts the water demand values for each hour throughout the week. Having forecasted all timesteps with the LSTM, a virtual online process can be implemented to enhance forecasting quality. |
| format | Article |
| id | doaj-art-d8cc8c244f0d4059bf44ee5459b3e0f3 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-d8cc8c244f0d4059bf44ee5459b3e0f32025-08-20T03:43:15ZengMDPI AGEngineering Proceedings2673-45912024-09-016914210.3390/engproc2024069042Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand ForecastingBruno Brentan0Ariele Zanfei1Martin Oberascher2Robert Sitzenfrei3Joaquin Izquierdo4Andrea Menapace5Hydraulic Engineering and Water Resources Department, School of Engineering, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, BrazilAIAQUA S.r.l., Via Volta 13/A, Bolzano 39100, ItalyDepartment of Infrastructure Engineering, Universität Innsbruck, 6020 Innsbruck, AustriaDepartment of Infrastructure Engineering, Universität Innsbruck, 6020 Innsbruck, AustriaInstitute for Multidisciplinary Mathematics, Universitat Politécnica de Valéncia, Valencia 46022, SpainFaculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano 39100, ItalyThis work proposes a cascade model incorporating Long–Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which offers a more reliable model to forecast short-term (hourly) and medium horizon (week) water demand. The MLP model integrates the previously forecasted demand with exogenous variables, functioning as a filter to enhance the accuracy of the LSTM estimation. The LSTM model estimates, utilizing a univariate approach, the hourly forecasting of water demand for the entire available dataset and the minimum night flow. The algorithm considers various time series sizes for each DMA and predicts the water demand values for each hour throughout the week. Having forecasted all timesteps with the LSTM, a virtual online process can be implemented to enhance forecasting quality.https://www.mdpi.com/2673-4591/69/1/42battle of water demand forecastinglong–short-term memorymulti-layer perceptron |
| spellingShingle | Bruno Brentan Ariele Zanfei Martin Oberascher Robert Sitzenfrei Joaquin Izquierdo Andrea Menapace Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting Engineering Proceedings battle of water demand forecasting long–short-term memory multi-layer perceptron |
| title | Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting |
| title_full | Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting |
| title_fullStr | Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting |
| title_full_unstemmed | Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting |
| title_short | Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting |
| title_sort | cascade machine learning approach applied to short term medium horizon demand forecasting |
| topic | battle of water demand forecasting long–short-term memory multi-layer perceptron |
| url | https://www.mdpi.com/2673-4591/69/1/42 |
| work_keys_str_mv | AT brunobrentan cascademachinelearningapproachappliedtoshorttermmediumhorizondemandforecasting AT arielezanfei cascademachinelearningapproachappliedtoshorttermmediumhorizondemandforecasting AT martinoberascher cascademachinelearningapproachappliedtoshorttermmediumhorizondemandforecasting AT robertsitzenfrei cascademachinelearningapproachappliedtoshorttermmediumhorizondemandforecasting AT joaquinizquierdo cascademachinelearningapproachappliedtoshorttermmediumhorizondemandforecasting AT andreamenapace cascademachinelearningapproachappliedtoshorttermmediumhorizondemandforecasting |