Air-conditioning usage conditional probability model for residential buildings

• Published in: Building and environment Vol. 81; pp. 172 - 182
• Main Authors: Ren, Xiaoxin, Yan, Da, Wang, Chuang
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2014; Elsevier
• Subjects: AC usage; Air conditioning; Applied sciences; Building technical equipments; Buildings; Buildings. Public works; Case study; Computation methods. Tables. Charts; Conditional probability; Conditional probability model; Construction; Descriptions; Environmental engineering; Exact sciences and technology; Mathematical analysis; Mathematical models; Occupant behavior; Residential building; Residential buildings; Series (mathematics); Structural analysis. Stresses; Types of buildings; Ventilation. Air conditioning; Case study; Occupant behavior; Residential building; Conditional probability model; AC usage; Air conditioning; Energy consumption; Use; Indoor climate; Measurement result; Conditional probability; Modeling; Survey; Resident; Continuous measurement; Probabilistic model; Behavior
• DOI: 10.1016/j.buildenv.2014.06.022
• ISSN: 0360-1323

This paper presents a residential building air-conditioning usage model based on actual occupant behavior. Surveys and continuous measurements of the AC usage of more than thirty families from 8 cities in different climate zones identified different AC usage patterns. The AC usage in residential buildings was found to be related to environment triggers, event triggers, and some random factors. An action-based quantitative stochastic model was then developed to predict AC usage in which typical patterns, including those driven by environmental triggers and event triggers, that affect AC usage are described mathematically as a series of conditional probabilities. Discrete three-parameter Weibull distributions are used in some of the model functions. The model is applied to three families as case studies with quantitative descriptions of the AC usage with different patterns. The studies show that the model works well for different behavioral patterns and also different conditions in the same pattern, and that the model predictions are also quite accurate. •Stochasticity, diversity, and complexity are shown in household AC usage.•A conditional probability model is set up based on continuous measurement to describe.•The model is action-based with combined environmental and event triggers.•3-parameter Weibull distribution gives the flexibility for applying on different patterns.•Three families, as case studies, show how the model works effectively and applicably.