MOGA design of temperature and relative humidity models for predictive thermal comfort
The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. One of these applications is predictive HVAC control, which aims to maintain thermal comfort while simultaneously minimizing the energy s...
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| Other Authors: | , |
| Format: | conferenceObject |
| Language: | eng |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10400.1/2176 |
| Country: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2176 |
| Summary: | The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. One of these applications is predictive HVAC control, which aims to maintain thermal comfort while simultaneously minimizing the energy spent, within a specified prediction horizon. Thermal comfort depends on several variables; among them inside temperature and relative humidity are key factors. In this paper the design of predictive neural network models for these two climate variables is discussed. The design approach uses a Multi-Objective Genetic Algorithms (MOGA) to determine the structure of the network, together with an efficient derivative-based estimation algorithm. Simulations with real weather and climate data show that excellent predictive models can be obtained with this methodology. |
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