Abstract
The use of grey-box models for short-time forecasting of buildings’ thermal behavior requires the determination of the models’ order since this order could influence the grey-box models’ performance. This paper presents an analysis of the optimal order of these models for different thermal conditions. The novelty of this work consists of considering the influence of the heating conditions on the determination of the performances of grey-box models. The analysis is based on experimental tests that were conducted in a room with different thermal conditions, related to the variation of the heating power. Experimental results were used for the determination of the optimal grey-box models’ order that minimizes the gap between the experimental results and the grey-box forecasting. Results show that the optimal grey-box models’ order depends on the buildings’ thermal conditions, but generally lies between two and three with an error less than 0.2 °C and a fit percent greater than 90%.
Highlights
Building energy simulation models require a good understanding of the thermal behavior of buildings [1]
The grey-box models provide some advantages in the buildings’ thermal modeling process, in particular, ease of their use and the possibility to link their parameters to global buildings’ physical characteristics, such as the heat resistance and the mass capacity. These models can be used for different purposes such as control of the indoor environment [8,9], forecasting energy consumption, and evaluating buildings’ energy performance [10,11,12]
[17], it was shown that the heating conditions considered in the determination of the optimal order of the grey-box models and that should the databedynamics affect determination of the optimal order of the grey-box models and that the data dynamics affect the performance of grey-box models
Summary
Building energy simulation models (white models) require a good understanding of the thermal behavior of buildings [1]. The grey-box models provide some advantages in the buildings’ thermal modeling process, in particular, ease of their use and the possibility to link their parameters to global buildings’ physical characteristics, such as the heat resistance and the mass capacity These models can be used for different purposes such as control of the indoor environment [8,9], forecasting energy consumption, and evaluating buildings’ energy performance [10,11,12]. In recent research [17], it was shown that the heating conditionsofshould be considered in the [17], it was shown that the heating conditions considered in the determination of the optimal order of the grey-box models and that should the databedynamics affect determination of the optimal order of the grey-box models and that the data dynamics affect the performance of grey-box models This paper discusses this issue using experimental tests conducted performance of grey-box models. This paper discusses issue using experimental conducted in various heating conditions for the investigation of thethis influence of these conditionstests on the optimal in various conditions order of theheating grey-box model. for the investigation of the influence of these conditions on the optimal order of the grey-box model
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