Occupant behavior controls a building’s energy system to adapt the indoor environment, significantly increasing building energy consumption. Occupant behavior, which refers to the occupancy inside a building and their interaction with building systems (windows, blinds, thermostats, lighting and appliances, etc.), has been largely overlooked in building energy performance analysis. These factors make it essential to design sustainable buildings. It is widely acknowledged in the literature that there is an alarming performance gap between the estimated and actual energy consumption in buildings. This paper proposes a systematic literature review on energy-related occupant behaviors and their implications for energy performance. It aims to better understand occupant behavior, existing behavior modeling approaches and their limitations, and key influential parameters on building energy performance. It is based on a survey of ScienceDirect, Web of science and Scopus scientific databases, using their bibliometric analysis tools together with the VOSviewer software. Finally, this study identifies the following significant research gaps for future development: limitations of the generic and robust occupant behavior model; lack of actual data for validation; lack of research on different types of buildings (institutions, university buildings); limitations of considering all factors which influence occupant behavior; missing the detailed realistic situations of occupant behavior; integrating building information modeling (BIM) into building energy modeling.

Flying insects have evolved to develop efficient strategies to navigate in natural environments. Yet, studying them experimentally is difficult because of their small size and high speed of motion. Consequently, previous studies were limited to tethered flights, hovering flights, or restricted flights within confined laboratory chambers. Here, we report the development of a cable-driven parallel robot, named lab-on-cables, for tracking and interacting with a free-flying insect. In this approach, cameras are mounted on cables, so as to move automatically with the insect. We designed a reactive controller that minimizes the online tracking error between the position of the flying insect, provided by an embedded stereo-vision system, and the position of the moving lab, computed from the cable lengths. We validated the lab-on-cables with Agrotis ipsilon moths (ca. 2 centimeters long) flying freely up to 3 meters per second. We further demonstrated, using prerecorded trajectories, the possibility to track other insects such as fruit flies or mosquitoes. The lab-on-cables is relevant to free-flight studies and may be used in combination with stimulus delivery to assess sensory modulation of flight behavior (e.g., pheromone-controlled anemotaxis in moths).

International audienceThis paper deals with the problem of modelling and controlling a multi-zone office building to ensure its thermal comfort by an optimal and cost effective management of the energy consumption. A thermal behaviour analysis of the building is carried out using the SimscapeTM library in MATLAB/SimulinkR environment that leads to a multi-model representation. Based on this modelling, the Yalmip toolbox in the MATLAB programming environment or an iterative optimization algorithm can be used to solve the control optimisation problem. The design of a model predictive control associated with a wise choice of the cost function makes it possible to obtain in simulation substantial energy benefits

Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learning (DRL) by learning their nonlinear dynamics. However, DRL methods often require extensive training time, and the resulting policies do not generalize well to different robot configurations or varying numbers of actuators. In this article, we introduce a novel DRL approach for controlling CDPRs that does not depend on the specific robot configuration. Our method trains an actuator-level policy that controls each motor to achieve its target cable length, in contrast to conventional DRL approaches that learn to control the entire robot to reach a desired end-effector position. To the best of our knowledge, this is the first work to apply DRL to control CDPRs using an actuator-level policy. This approach offers two main advantages: (i) a single shared policy can be applied to any CDPR configuration, regardless of actuator count, and (ii) reliance on inverse kinematics, avoiding the more challenging forward kinematics problem. Training is performed in simulation, and the learned policy is successfully transferred to a real CDPR. Experimental results show that the actuator-level policy (ALP) surpasses traditional reinforcement learning methods in both robustness and precision.