A psychological model for the prediction of energy‐relevant behaviours in buildings: Cognitive parameter optimisation. Issue 1 (4th February 2022)

• Record Type: Journal Article
• Authors: von Grabe, Jörn; Korsavi, Sepideh
• Is Part Of: Cognitive computation and systems. Volume 4:Issue 1(2022)
• Page Start: 45
• Page End: 67
• Publication Date: 2022-02-04
• Subjects: cognitive modelling -- energy‐relevant behaviour -- instance‐based learning -- prediction; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12042
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: Energy consumption in buildings is a major contributor to global warming and therefore has become a field of intensive research. This type of energy consumption can be described in two dimensions: an appliance‐based dimension and a behaviour‐based dimension. To address the behaviour‐based dimension a recent study proposed a cognitive human‐building interaction model that builds on the instance‐based learning paradigm. However, since the values of the standard cognitive parameters commonly used for modelling lab‐based behaviours are not suitable for the 'real‐world' domain of human‐building interaction, this paper aims to identify cognitive parameter values adapted to and suitable for the specific character of this application domain. To achieve this goal, a virtual test environment—consisting of an occupied room and a corresponding model task—was designed to test the performance of the model and its dependence on a set of fundamental cognitive parameters. A test criterion was developed that did not depend on empirical data but used the predictive consistency of the model as reference. A range of values was pre‐selected for each parameter based on theoretical and empirical considerations, which was then tested against the evaluation criterion. The performance of the model was improved significantly throughout the parametrisation process and yielded plausible results.