A data-driven approach for steam load prediction in buildings
Predicting building energy load is important in energy management. This load is often the result of steam heating and cooling of buildings. In this paper, a data-driven approach for the development of a daily steam load model is presented. Data-mining algorithms are used to select significant parame...
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| Published in | Applied energy Vol. 87; no. 3; pp. 925 - 933 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier Ltd
01.03.2010
Elsevier |
| Series | Applied Energy |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Predicting building energy load is important in energy management. This load is often the result of steam heating and cooling of buildings. In this paper, a data-driven approach for the development of a daily steam load model is presented. Data-mining algorithms are used to select significant parameters used to develop models. A neural network (NN) ensemble with five MLPs (multi-layer perceptrons) performed best among all data-mining algorithms tested and therefore was selected to develop a predictive model. To meet the constraints of the existing energy management applications, Monte Carlo simulation is used to investigate uncertainty propagation of the model built by using weather forecast data. Based on the formulated model and weather forecasting data, future steam consumption is estimated. The latter allows optimal decisions to be made while managing fuel purchasing, scheduling the steam boiler, and building energy consumption. |
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| AbstractList | Predicting building energy load is important in energy management. This load is often the result of steam heating and cooling of buildings. In this paper, a data-driven approach for the development of a daily steam load model is presented. Data-mining algorithms are used to select significant parameters used to develop models. A neural network (NN) ensemble with five MLPs (multi-layer perceptrons) performed best among all data-mining algorithms tested and therefore was selected to develop a predictive model. To meet the constraints of the existing energy management applications, Monte Carlo simulation is used to investigate uncertainty propagation of the model built by using weather forecast data. Based on the formulated model and weather forecasting data, future steam consumption is estimated. The latter allows optimal decisions to be made while managing fuel purchasing, scheduling the steam boiler, and building energy consumption. |
| Author | Zhang, Zijun Li, Mingyang Kusiak, Andrew |
| Author_xml | – sequence: 1 givenname: Andrew surname: Kusiak fullname: Kusiak, Andrew email: andrew-kusiak@uiowa.edu – sequence: 2 givenname: Mingyang surname: Li fullname: Li, Mingyang – sequence: 3 givenname: Zijun surname: Zhang fullname: Zhang, Zijun |
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| Keywords | Steam load prediction Parameter selection Neural network ensemble Data mining Building load estimation Energy forecasting Monte Carlo simulation Energy consumption Monte Carlo method Steam heating Uncertainty Cooling Buildings Climatic data Neural network Algorithm Forecasting Simulation Energy management |
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| Snippet | Predicting building energy load is important in energy management. This load is often the result of steam heating and cooling of buildings. In this paper, a... |
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| SubjectTerms | Applied sciences Building load estimation Data mining Data mining Building load estimation Steam load prediction Neural network ensemble Energy forecasting Monte Carlo simulation Parameter selection Economic data Energy Energy economics Energy forecasting Energy policy Exact sciences and technology General, economic and professional studies Methodology. Modelling Monte Carlo simulation Neural network ensemble Parameter selection Steam load prediction |
| Title | A data-driven approach for steam load prediction in buildings |
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