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.
Highlights
Introduction and Related WorksIn the context of the perspicacious decrease of fossil fuel resources and ongoing increase of energy consumption innate to the intensification of human urban activities during the last decades, the management of energy consumption in commercial and residential buildings has become a vital question
The sensors’ quality and the technological features of the remote devices forming the physical part of the automated or smart buildings play an undeniable role in the performance of smart building energy management systems (SBEMS) in optimizing the building’s energy consumption, the primary inefficiency of such systems in declining energy consumption is related to the quality of the models that bear either the identification of the relationship between the building’s behavior and the controller that hatches up the actions of implemented sensors and remote devices or to the excellence of the control strategy in charge of the building’s behavior control
On the basis of the above-mentioned points, in the present article, we focus on 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 of the heated space
Summary
Introduction and Related WorksIn the context of the perspicacious decrease of fossil fuel resources and ongoing increase of energy consumption innate to the intensification of human urban activities during the last decades, the management of energy consumption in commercial and residential buildings has become a vital question. Regarding the works of [1] and [2], in the USA, the contribution of energy consumption in space heating was responsible for 43 percent in residential buildings in 2015, and in commercial buildings, this contribution was about 25 percent in 2012 This shows the huge slice of energy consumption related to space heating in the above-mentioned two sections. The diversity of the involved factors (parameters) as well as their highly nonlinear variation make the identification and modeling of the dynamic behavior of a building a challenging task Within this context and by considering that besides the living space’s intrinsic structural features, the occupation of the living space (by users or residents) may affect the model of heating dynamics of the concerned living space, we have investigated the design, implementation, and validation of a data-driven machine learning-based identifier supplied by the time-series prediction paradigm’s formalism. A human in a sitting position and at about 1.80 m in height can emit 100 watts [4,5,6]
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