Sustainable Energy Management of Institutional Buildings through Load Prediction Models: Review and Case Study
Institutional buildings need smart techniques to predict the energy consumption in a smart grids’ framework. Here, the importance of dynamic load forecasting as a tool to support the decision in smart grids is addressed. In addition, it is reviewed the energy consumption patterns of institutional bu...
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| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2021
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/72964 |
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| Summary: | Institutional buildings need smart techniques to predict the energy consumption in a smart grids’ framework. Here, the importance of dynamic load forecasting as a tool to support the decision in smart grids is addressed. In addition, it is reviewed the energy consumption patterns of institutional buildings and the state-of-the-art of load forecast modeling using artificial neural networks. The discussion is supported by historical data from energy consumption in a university building. These data are used to develop a reliable model for the prediction of the electric load in a campus. A neural network model was developed, which can forecast the load with an average error of 6.5%, and this model can also be used as a decision tool to assess the convenience of supplying this load with a set of renewable energy sources. Statistical data that measure the availability of the local renewable sources can be compared with a load model in order to assess how well these energy sources match the energy needs of buildings. This novel application of load models was applied to the campus where a good correlation (Pearson coefficient of 0.803) was found between energy demand and the availability of the solar resource in the campus. |
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| ISBN: | 978-1-78985-877-8 |
| DOI: | 10.5772/intechopen.93425 |