English

Multidimensional effects through porous building elements is barely explored in the literature due to many difficulties such as modeling complexity, computer run time, numerical convergence and highly moisture-dependent properties. Furthermore, when the multidimensional effect is considered, thermal bridges may play an important role on the hygrothermal building performance due to local increase of heat and mass flux densities.

Windows are the only part in buildings that can directly penetrate the solar radiation into the space and thus the shading devices are needed to control the solar penetration. In this study, optimized slat angle control strategies of venetian blinds are suggested. In the development process of the slat angle control strategy, annual cooling, heating, lighting energy and Daylight Glare Index performances are evaluated for each slat angle in the first hand, and then those comprehensive performances are categorized under different window solar radiation regions with the increment of 100 W/m2.

Model Predictive control is an advanced control tech-nique that has been used to optimize thermal comfort in buildings. Nowadays, the new buildings are char-acterized by an important inertia as well as low power heating systems. Since the thermal losses are very low, taking into account the intermittent occupancies in the control strategy is questionable. More precisely, in this paper, two model predictive controllers are devel-oped to reduce energy consumption while preserving the thermal comfort.

This paper reports on a study of the thermal performance of two-storey apartments in Adelaide, South Australia. The overall design achieved an energy rating score around 7.5 Stars in the Australia’s National Home Energy Rating Scheme (NatHERS); however, without air-conditioning some of the spaces were considered too warm during hot weather. Aspects of the building design and operation that contribute to poor performance are investigated.

Some building performance simulation programs compute the volume flow rate in ducts and pipe net-works for a given fan speed, which may be computed by a feedback controller, based on the intersection of the flow resistance and fan curve. We show ana-lytically and based on numerical simulations that fan models that use affinity laws and polynomials for the fan curve can lead to equations that become singular, have no, one, multiple or an infinite number of solu-tions.

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.