A methodology for identifying and improving occupant behavior in residential buildings

This paper reports the development of a methodology for identifying and improving occupant behavior in existing residential buildings. In this study, end-use loads were divided into two levels (i.e. main and sub-category), and they were used to deduce corresponding two-level user activities (i.e. general and specific occupant behavior) indirectly. The proposed method is based on three basic data mining techniques: cluster analysis, classification analysis, and association rules mining. Cluster analysis and classification analysis are combined to analyze the main end-use loads, so as to identify energy-inefficient general occupant behavior. Then, association rules are mined to examine end-use loads at both levels, so as to identify energy-inefficient specific occupant behavior. In order to demonstrate its applicability, this methodology was applied to a group of residential buildings in Japan, and one building with the most comprehensive household appliances was selected as the case building. The results show that, for the case building, the method was able to identify the behavior which needs to be modified, and provide occupants with feasible recommendations so that they can make required decisions. Also, a reference building can be identified for the case building to evaluate its energy-saving potential due to occupant behavior modification. The results obtained could help building occupants to modify their behavior, thereby significantly reducing building energy consumption. Moreover, given that the proposed method is partly based on the comparison with similar buildings, it could motivate building occupants to modify their behavior

The impact of occupants’ behaviours on building energy analysis: A research review

Over the past 15 years, the evaluation of energy demand and use in buildings has become increasingly acute due to growing scientific and political pressure around the world in response to climate change. The estimation of the use of energy in buildings is therefore a critical process during the design stage. This paper presents a review of the literature published in leading journals through Science Direct and Scopus databases within this research domain to establish research trends, and importantly, to identify research gaps for future investigation. It has been widely acknowledged in the literature that there is an alarming performance gap between the predicted and actual energy consumption of buildings (sometimes this has been up to 300% difference). Analysis of the impact of occupants’ behaviour has been largely overlooked in building energy performance analysis. In short, energy simulation tools utilise climatic data and physical/ thermal properties of building elements in their calculations, and the impact of occupants is only considered through means of fixed and scheduled patterns of behaviour. This research review identified a number of areas for future research including: larger scale analysis (e.g. urban analysis); interior design, in terms of space layout, and fixtures and fittings on occupants’ behaviour; psychological cognitive behavioural methods; and the integration of quantitative and qualitative research findings in energy simulation tools to name but a few

Development and improvement of occupant behavior models towards realistic building performance simulation: A review

With the rise of concern about newly-designed or retrofitted buildings to have robust performance under different realistic scenarios, it is of vital importance to providing reliable energy predictions for building design and planning. Occupant behavior (OB), as one source of the significant uncertainties, is generally oversimplified as static schedules or predetermined inputs, which could cause a significant gap between the simulated and measured one. To bridge such gap, growing interests have been raised to understand the role of OB on building energy performance and develop OB models which can be integrated into building simulation tools. This paper aims to provide a systematic review with the focus on three important issues: a) the impact uncertainty caused by OB in building performance simulation and their differences in various spatial scales and temporal granularities; b) main criteria for the comparison and selection of modeling methods; c) requisite considerations to improve the performance of OB models. Based on this review, a framework was proposed towards improving the predictive performance of future OB models. Existing research gaps and key challenges for OB modeling are identified and future directions in this area are highlighted

A novel methodology for knowledge discovery through mining associations between building operational data

Nowadays, vast amounts of data on building operation and management have been collected and stored. However, the data is rarely translated into useful knowledge about building energy performance improvement, due mainly to its extreme complexity and a lack of effective data analysis techniques. This paper reports the development of a new methodology for examining all associations and correlations between building operational data, thereby discovering useful knowledge about energy conservation. The method is based on a basic data mining technique (association rule mining). To take full advantage of building operational data, both daily and annual time periods should be mined. Moreover, data from two different years should be mined, and the obtained associations and correlations in the two years should be compared. In order to demonstrate the applicability of the proposed method, the method was applied to the operational data of the air-conditioning system in a building located in Montreal. The results show energy waste in the air-conditioning system as well as equipment faults. A low/no cost strategy for saving energy in the system operation was also proposed. The results obtained could help to better understand building operation and provide opportunities for energy conservation.Peer reviewed: YesNRC publication: Ye

Analysis on the driving factors and patterns of window opening and closing behaviour in French households

International audienc

Enhancing a vertical earth-to-air heat exchanger system using tubular phase change material

International audienc

Experimental investigation of a vertical earth-to-air heat exchanger system

International audienc

Numerical modeling and parametric study of a vertical earth-to-air heat exchanger system

International audienc

Development of event-driven optimal control for central air-conditioning systems

Event-driven optimal control was recently developed for central air-conditioning systems to speed up the response of optimal control to irregular changes in the system optioning conditions. In a time-driven paradigm usually, the optimization is carried out with a constant frequency, however the event-driven optimal control triggers optimization actions by events, which will be essentially defined to catch up with the irregular changes. Considering that the occurrence of events should imply the necessity to execute optimization, this paper investigates the necessity of optimization actions, based on which a new method to develop event-driven optimal control law is proposed. This can naturally lead to the establishment of an event-action map. This map indicates that not all the decision variables should be optimized when an eventoccurs, different from other methods that require optimizing all decision variables. The merits of the new method were also demonstrated using several case studies.<br