Data-Driven Living Spaces' Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification.
Sensors (Basel)
; 20(4)2020 Feb 16.
Article
em En
| MEDLINE
| ID: mdl-32079104
ABSTRACT
Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces' heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space's occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building's living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron's (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building's living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
Base de dados:
MEDLINE
Assunto principal:
Fatores Socioeconômicos
/
Indústria da Construção
/
Aprendizado de Máquina
/
Calefação
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Aspecto:
Determinantes_sociais_saude
/
Equity_inequality
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2020
Tipo de documento:
Article