•An optimization of a tradeoff between energy cost and PMV index is addressed.•A piecewise linearization-based approximate PMV model is developed.•A model-based periodic event-triggered mechanism ...(ETM) is developed.•The ETM method results in a significant reduction in computational resources.
This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic event-triggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control.
•Combination of the genetic algorithm with the Latin Hypercube sampling process to investigate the robustness of a building optimization process.•The objective of this study is to clarify the ...influence of the occupant behavior on the robustness of the results of a computer-based building optimization process.•A clear tendency toward a parameter setting or a range of parameter settings is observed for all parameters to be optimized and the results are robust.•For linear parameters, the mean value of the recommendation frequency of the parameter settings is the best indicator for the recommendation process, and the variance is shown to be a good indicator for its robustness.•For non-linear parameter in the search space, the most recommended parameter setting in a sampling run is the preferred indicator rather than the mean value.
Optimization algorithms like the genetic algorithm (GA) have been used for computer-based building design decision support methods in the last decade. However, the robustness and reliability of the results of these optimization processes have not been quantified. Furthermore, the influence of input parameters with uncertainties, like the occupant behavior on the results of a building optimization process, has not been evaluated.
In this study, the genetic algorithm is run repeatedly and is combined with the Latin Hypercube sampling method to evaluate the results of a building optimization problem with varying input variables of the internal heat gains, occupant density and user schedules.
The objective is to (1) clarify the influence of the occupant behavior on the outcome of a computer-based building optimization process, and (2) propose a method to define the uncertainties of a building optimization process.
Rsesults illustrate a clear tendency toward an optimal solution of the optimization process with varying input variables. All results are described as robust or relatively robust, which shows that the evaluation of the robustness depends on the nature of the parameter to be optimized.
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