On the multi-agent stochastic simulation of occupants in buildings

Abstract

One of the principle causes for deviations between predicted and simulated performance of buildings relates to the stochastic nature of their occupants: their presence, activities whilst present, activity dependent behaviours and the consequent implications for their perceived comfort. A growing research community is active in the development and validation of stochastic models addressing these issues; and considerable progress has been made. Specifically models in the areas of presence, activities while present, shading devices, window openings and lighting usage.One key outstanding challenge relates to the integration of these prototype models with building simulation in a coherent and generalizable way; meaning that emerging models can be integrated with a range of building simulation software. This thesis describes our proof of concept platform that integrates stochastic occupancy models within a multi agent simulation platform, which communicates directly with building simulation software. The tool is called Nottingham Multi-Agent Stochastic Simulation (No-MASS).No-MASS is tested with a building performance simulation solver to demonstrate the effectiveness of the integrated stochastic models on a residential building and a non-residential building. To account for diversity between occupants No-MASS makes use of archetypical behaviours within the stochastic models of windows, shades and activities. Thus providing designers with means to evaluate the performance of their designs in response to the range of expected behaviours and to evaluate the robustness of their design solutions; which is not possible using current simplistic deterministic representations. No-MASS employs agent machine learning techniques that allow them to learn how to respond to the processes taking place within a building and agents can choose a strategy without the need for context specific rules.Employing these complementary techniques to support the comprehensive simulation of occupants presence and behaviour, integrated within a single platform that can readily interface with a range of building (and urban) energy simulation programs is the key contribution to knowledge from this thesis. Nevertheless, there is significant scope to extend this work to further reduce the performance gap between simulated and real world buildings.

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

“…(Perrels and Weber, 2000) analysed the impact of lifestyles determined by socio-demographic variables (age, income, education level) on energy demand and demonstrated that final energy consumption for a stagnation-type household is 1.5 times higher than for a sustainable through reflective consumption household. Furthermore, social interactions are an essential consideration (Chapman, 2017) since they lead to decisions on a household level (e.g., opening windows) that differs from what an agent alone would choose (e.g., occupant bothered by the cold).…”

“…However, these platforms may hinder coupling with other existing physical models. The decision-making process is based on probabilities in forty per cent of the studies (Alfakara, 2010;Azar and Menassa, 2010;Tröndle and Choudhary, 2017;Abdallah, Basurra and Gaber, 2018;Chapman, Siebers and Robinson, 2018).…”

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

“…They compared Haldi's shade model with other schedule and rule-based shade models, however, this research did not consider cooling and heating energy uncertainty. A few researchers [21,22] simulated building energy performance by considering different occupant behaviors (such as window opening, shade adjustment, and occupancy), however, the energy uncertainty resulting from manual shades alone cannot be directly obtained from these studies since they only gave the overall impact of different occupant behaviors. Additionally, they all used Haldi's shade model, which is an unusual shade configuration, as mentioned above.…”

“…Therefore, the Monte Carlo analysis was adopted to analyze the uncertainty of energy performance. As described in [22], additional simulation time needed for Monte Carlo can be considered as a weakness. Thus, this study calculates the required minimum number of simulations according to the graphical method recommended in [25].…”

On the sensitivity to different aspects of occupant behaviour for selecting the appropriate modelling complexity in building performance predictions

“…Noteworthy studies that try to combine multiple aspects to derive a simulation framework are rare (Chapman, Siebers, and Robinson 2014;Rysanek and Choudhary 2015;Tanimoto, Hagishima, and Sagara 2008). However, those that do exist tend to adopt one complexity level for all aspects of OB, without considering their relative importance.…”