Towards a general framework for an observation and knowledge based model of occupant behaviour in office buildings

This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour withprobabilistic cause-effect relations based not only on previous works, but also with conditional probabilities coming either from expert knowledge or deduced from observations. The approach has been used in the co-simulation of building physics and human behaviour in order to assess the CO 2 concentration in an office.Comment: IBPC 2015 Turin , Jun 2015, Turin, Italy. 201

Estimating Occupancy in Residential Context Using Bayesian Networks for Energy Management

International audienceA general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates

Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality

This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour with probabilistic cause-effect relations based on knowledge, but also with conditional probabilities coming either from knowledge or deduced from observations. This approach has been applied to the co-simulation of the CO2 concentration in an office coupled with human behaviour.Comment: IBPSA India 2015, Dec 2015, Hyderabad, India. arXiv admin note: substantial text overlap with arXiv:1510.0197

Explanations Engine For Energy Management Systems in Buildings

International audienceHumans live and spent their times in buildings. Determining the best configuration for their office or apartment (HVAC configuration, doors and windows positions, usage of appliances,...) is becoming subtle because low consumption buildings are becoming more and more sensitive to human behavior. Moreover, variable energy costs and energy availability issues increase the complexity of the energy management problem. In such complex situations, many scientific research and engineering works are ongoing with the aim of supporting occupants in their everyday life, but still, in most cases, there is a need for experts to design models for living zones to manage, which is time-consuming and very costly. This paper will continue in the stream to help occupants to understand their energy systems and the impact of their actions on the system, by providing causal explanations and presenting a path diagram for all actions and environmental changes and their consequences

Differential Explanations for Energy Management in Buildings

International audienceIn the field of building energy efficiency, researchers generally focus on building performance and how to enhance it. The objective of this work is to empower the building occupants by putting them in the loop of efficient energy use, supporting them to achieve their objectives by pointing out how far their actions are from an optimal set of actions. Different levels of explanation are investigated. Indicators measuring the distance to optimality are, firstly, proposed. An algorithm that generates deeper explanations is then presented to determine how changing some actions impacts comfort. The paper emphasizes the importance of explanations with a real case study. It identifies the type and level of explanations needed for different occupants. The concept of replay is presented. An occupant can replay his past actions and learn from them

Decision tree and Parametrized classifier for Estimating occupancy in energy management

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Decision tree and Parametrized classifier for Estimating occupancy in energy management

International audienc

Assessing Energy Strategies in Active Buildings considering Human Behaviour

International audienceIn the recent years, surveys and studies have established the importance of occupant's behaviour on energy consumption in buildings. Therefore, inclusion of in-habitants' behaviours is compulsory for the assessment of building energy management system's (BEMS) strategies, which highly depends on human behaviour. The purpose of modelling the inhabitants behaviour is to see how their choices and control of household appliances can impact the energy consumption. In this paper, a co-simulation approach is presented where the inhabitants' behaviours are co-simulated with the SIMBAD-MOZART thermal model of a reference house and BEMS. The realization of all the different kinds of inhabitant behaviours into energy co-simulations will help to improve the smart grid technology and hence provide inhabitants with better services to save energy and cost while maintaining their comfort levels

An occupant-centered approach to improve both his comfort and the energy efficiency of the building

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