Adaptive Domotic System in Green Buildings
This paper presents an adaptive domotic system in green buildings. In our case, the data of sensor and devices were controlled in CIESOL center. The adaptive domotic system uses a Fuzzy Lattice Reasoning classifier for predicting building energy performance depending on the user condition. Training...
| Main Authors: | , , |
|---|---|
| Format: | info:eu-repo/semantics/report |
| Language: | English |
| Published: |
2016
|
| Online Access: | http://hdl.handle.net/10835/3871 |
| _version_ | 1789406621245374464 |
|---|---|
| author | Rodríguez-Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando |
| author_facet | Rodríguez-Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando |
| author_sort | Rodríguez-Gracia, Diego |
| collection | DSpace |
| description | This paper presents an adaptive domotic system in green buildings. In our case, the data of sensor and devices were controlled in CIESOL center. The adaptive domotic system uses a Fuzzy Lattice Reasoning classifier for predicting building energy performance depending on the user condition. Training and testing of classifiers were carried out with temperature condition data acquired for 4 months (February, May, July and November) in the case building called CIESOL. The results show
a high accuracy rates with a mean absolute error between 0% and 0.21% |
| format | info:eu-repo/semantics/report |
| id | oai:repositorio.ual.es:10835-3871 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2016 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-38712023-04-12T19:27:32Z Adaptive Domotic System in Green Buildings Rodríguez-Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando This paper presents an adaptive domotic system in green buildings. In our case, the data of sensor and devices were controlled in CIESOL center. The adaptive domotic system uses a Fuzzy Lattice Reasoning classifier for predicting building energy performance depending on the user condition. Training and testing of classifiers were carried out with temperature condition data acquired for 4 months (February, May, July and November) in the case building called CIESOL. The results show a high accuracy rates with a mean absolute error between 0% and 0.21% 2016-01-18T09:25:01Z 2016-01-18T09:25:01Z 2016-01-18 info:eu-repo/semantics/report http://hdl.handle.net/10835/3871 en info:eu-repo/semantics/openAccess 4th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2015), July 12-16, Okayama, Japan |
| spellingShingle | Rodríguez-Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando Adaptive Domotic System in Green Buildings |
| title | Adaptive Domotic System in Green Buildings |
| title_full | Adaptive Domotic System in Green Buildings |
| title_fullStr | Adaptive Domotic System in Green Buildings |
| title_full_unstemmed | Adaptive Domotic System in Green Buildings |
| title_short | Adaptive Domotic System in Green Buildings |
| title_sort | adaptive domotic system in green buildings |
| url | http://hdl.handle.net/10835/3871 |
| work_keys_str_mv | AT rodriguezgraciadiego adaptivedomoticsystemingreenbuildings AT piedrafernandezjoseantonio adaptivedomoticsystemingreenbuildings AT iribarnemartinezluisfernando adaptivedomoticsystemingreenbuildings |