IBPSA2013

Building performance simulation is being increasingly deployed beyond the building design phase to support building operation. Specifically, the predictive feature of the simulation-assisted building systems control strategy provides distinct advantages in view of building systems with high latency and inertia. Such advantages could be exploited only if model predictions could be relied upon. Hence, it is important to calibrate simulation models based on monitored data.

A framework entitled Evolutionary Energy Performance Feedback for Design (EEPFD) was developed to mobilize the potential of multidisciplinary design optimization (MDO) towards solving current obstacles between design and energy performance feedback. However, EEPFD needs to be applicable to the early stage design process where it has the potential for the greatest impact on the overall building lifecycle performance.

This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus models. A Bayesian approach can quantify uncertainty in uncertain parameters while updating their values given measurement data. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables researchers to rigorously validate the accuracy of calibration results in terms of both calibration parameter values and calibrated model predictions.

We present a method for predicting visual comfort conditions of occupants in daylit spaces. Using an online survey, 67 occupants of a multi-story open plan studio space evaluated long-term visual comfort at their workstations in a substantially daylit space which is known to have glare problems. Visual comfort simulations of each occupant’s specific location were conducted and compared to the survey results. Simulations included discomfort glare, monitor contrast, visibility of the sun in the field of view and the presence of direct light on the workplane.

Smart homes and Ambient Intelligence Environments require embedded technologies able to work and think jointly. Indeed, home technologies are expected to form together an individual supportive system whose cooperation and context awareness capabilities make it more than the sum of the different parts (equipments, functionalities, services, appliances, etc.).

Due to the development of energy performance contracting and the needs for peak electric demand reduction, the interest for optimal building control is renewed. In this context, the real time prevision and optimization of building heat demand can help the manager to reduce the energy bill and to propose peak shaving offers. Our study aims to illustrate such heat control strategies on a one floor elementary school. The building is modeled through a second order inverse “grey box” model.

This paper proposes a modeling and analysis convention for a side- and/or toplit space called the ‘reference office’. The reference office is meant to act as a baseline for comparative analyses of different façade and/or electric lighting technologies and may be used in research (facilitating the comparison of results from multiple studies) in practice (for product rating) as well as in architectural education (enabling students to contrast their design ideas to a set of standard design solutions).

The present paper analyses a case study of the application of dynamic energy simulation on the energy efficiency improvement process of an existing commercial building, the retrofit of a CHP machine for the combined generation of heat and power is analysed.