Semantic Diagnosis Approach for Buildings
| Autor: | Anika Schumann, Bei Chen, Joern Ploennigs, Michael Maghella |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science 020209 energy 020208 electrical & electronic engineering HVAC control system ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Atmospheric model Computer Science Applications law.invention Data modeling Reliability engineering Control and Systems Engineering law HVAC Ventilation (architecture) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Electrical and Electronic Engineering business Information Systems Building automation Efficient energy use |
| Zdroj: | IEEE Transactions on Industrial Informatics. 13:3399-3410 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2017.2726001 |
| Popis: | The detection and diagnosis of abnormal building behavior is key to further improve the comfort and energy efficiency in buildings. An increasing number of sensors can be utilized for this task but these lead to higher integration effort and the need to capture the sensor interactions. This paper presents a novel diagnostic approach for buildings with complex heating, ventilation, air-conditioning (HVAC) systems. It uses semantic graphs to automatically create the diagnostic model from the building's data points and to identify potential cause-effect-relationships based on past and current time series data. The approach is validated on various simulated examples of a multiroom HVAC control system. The experimental results show that it can diagnose multiple faults with and without delays with high accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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