Abstract
This paper presents a structured, generically applicable, method for using building performance simulation to aid the development of comfort-driven solar shading controls by mapping predicted occupant comfort conditions to sensor measurements. The method uses confusion matrices as a statistical classification approach to facilitate (i) selection of sensor deployment strategies that offer beneficial trade-offs considering multiple performance aspects and (ii) identification of control algorithms that optimise comfort conditions using non-ideal sensors. The support method requires relatively little effort from a developer, only a small number of simulations and fits well within the current practice of shading control development. The method is tested using a sun-tracking control strategy for indoor roller blinds as a case study, which demonstrates that the method can identify high-performance solutions. Finally, generally applicable features of the method are extrapolated from the case study, and alternative applications and the method’s limitations are discussed.
Highlights
Automated solar shading systems are instrumental for improving indoor environmental quality and reducing building energy consumption (Beck and Dolmans 2010; Daum and Morel 2010; Konis and Selkowitz 2017; Kuhn 2017; Lee, DiBartolomeo, and Selkowitz 1998; Shen and Tzempelikos 2012)
This paper presents a structured, generically applicable, method for using building performance simulation to aid the development of comfort-driven solar shading controls by mapping predicted occupant comfort conditions to sensor measurements
This research presented a method that structures the use of building performance simulation (BPS) to support the development of comfort-driven control strategies for automated solar shading systems
Summary
Automated solar shading systems are instrumental for improving indoor environmental quality and reducing building energy consumption (Beck and Dolmans 2010; Daum and Morel 2010; Konis and Selkowitz 2017; Kuhn 2017; Lee, DiBartolomeo, and Selkowitz 1998; Shen and Tzempelikos 2012). The number of possible sequences of shading system actuations or states defines a vast control space Leveraging these parameters in the development of comfort-driven control systems is complex because it requires insight into comfort conditions of occupants and trade-offs between conflicting performance aspects (Loonen et al 2013). Exhaustive-search simulation studies (Yun, Park, and Kim 2017) and self-learning methods (Gunay et al 2014) have been applied to relate sensor measurements to performance goals by optimizing control thresholds for simple RBC strategies. This optimization does require, exploring a vast space of possible control thresholds. These conclusions extrapolate the generic features and benefits of the support method from the case study and suggest promising future applications
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