Advanced detection of HVAC faults using unsupervised SVM novelty detection and Gaussian process models
A novel fault detection algorithm based on machine learning is introduced in this paper, that is applied to the detection of faults in heater, ventilation and air conditioning (HVAC) systems. The algorithm is based on the use of a set of nonlinear regressors intended to estimate the response of the...
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| Veröffentlicht in: | Energy and buildings 2017-08, Vol.149 (C), p.216-224 |
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| container_title | Energy and buildings |
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| creator | Van Every, Philip Michael Rodriguez, Mykel Jones, C. Birk Mammoli, Andrea Alberto Martínez-Ramón, Manel |
| description | A novel fault detection algorithm based on machine learning is introduced in this paper, that is applied to the detection of faults in heater, ventilation and air conditioning (HVAC) systems. The algorithm is based on the use of a set of nonlinear regressors intended to estimate the response of the HVAC to the external variables. The regression algorithm is the well known Gaussian process regression, which, through a Gaussian modeling of the parameter priors and the conditional likelihood of the observations, is able to produce a probabilistic model of the prediction. We use the prediction error and its estimated variance as an input to a support vector machine novelty detector that, in an unsupervised way, is able to detect the faults of the HVAC. This algorithm improves the standard novelty detection, as it can be seen in the experiments. |
| doi_str_mv | 10.1016/j.enbuild.2017.05.053 |
| format | Article |
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| ispartof | Energy and buildings, 2017-08, Vol.149 (C), p.216-224 |
| issn | 0378-7788 1872-6178 |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Air handling unit Building envelopes Buildings Case studies Cladding Climate Climate change Computer simulation Cooling Cooling loads Cooling systems Data processing Energy consumption Energy demand Energy modeling Energy usage Envelopes Fault detection Gaussian process neural networks Housing HVAC Life cycle analysis Life cycle assessment Lifestyles Novelty detection One-class support vector machine Residential areas Residential buildings Residential energy Sensitivity analysis Sustainability Sustainable development Thermal energy |
| title | Advanced detection of HVAC faults using unsupervised SVM novelty detection and Gaussian process models |
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