Abstract
The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.
Highlights
The need for the development of a sustainable environment is of the utmost importance to reduce the effects of climate change and global temperature increase due to the significant rise in the emissions of greenhouse gasses (GHGs) such as CO2, CH4, N2 O, HFC, PFC and SF6
According to the International Energy Agency (IEA) [1,2], the buildings and construction sector is responsible for almost 35% of global primary energy consumption, which is much higher than the other sectors, i.e., transport (28%), and industries
The increase in emissions from the buildings sector is attributable to the continued use of coal, oil, and natural gas for heating and cooking, combined with rising activity levels in locations where electricity remains carbon-intensive, resulting in stable direct emissions but increasing indirect emissions, i.e., electricity [3]
Summary
The need for the development of a sustainable environment is of the utmost importance to reduce the effects of climate change and global temperature increase due to the significant rise in the emissions of greenhouse gasses (GHGs) such as CO2 , CH4 , N2 O, HFC, PFC and SF6. The integration of renewable energy into buildings leads to a reduction in energy dependency, it cannot be considered for a complete reduction in energy consumption and emissions This transformation is based mostly on commercially/industrially accessible technologies, including improved envelopes for new and existing buildings [8], e.g., phase-change materials [9], highly insulating materials [10], heat pumps [11], energy-efficient appliances [12], occupancy behavior analysis [13], and intelligent building controllers [14]. Buildings are heterogeneous systems (complex networks of appliances and sensors) and the necessity of multi-objective applications for controllers made their design and development extremely challenging and time-consuming These real-time controllers are model-based controllers and their performance accuracy is greatly dependent on the building model accuracy. Steady-state modeling is useful when there is not much data available and for long-duration energy analysis [28]
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