IBPSA2013

This paper presents a method using a spectral approach to simulate a hybrid ground-coupled heat pump system. At each time step, the algorithm computes the ground load based on the building load, the coefficient of performance and the capacity of the heat pumps (which are a function of the fluid temperature), and the equivalent borehole thermal resistance. To capture the nonlinearity between the fluid temperature and the ground load, an iterative process is used.

High-order models of building performance are the preferred tool, but their complexity and time requirements can cause difficulties when attempting to create large sets of models or when used by non-experts. Reduced-order models require fewer inputs and are simpler, but typically sacrifice accuracy for speed.

Net-zero energy building (NZEB) is thought to be the building of choice, but in practice, is also synonym  to high investment cost. It is, therefore, very important to investigate if the amount of the additional capital investment could be recouped from the energy saving (or generation). The investigation is particularly meaningful for industrial halls for the great energy saving potential (with respect to the high energy demand) and the ready energy generation possibility (due to favourable building geometry).

Building performance simulation often fails to predict accurately the real energy performance, mostly due to great uncertainties in the input data. Errors in com-puted performance are particularly significant in the case of existing buildings, for which the amount of in-formation about intrinsic characteristics is low. How-ever, efficient energy retrofit operations make neces-sary an accurate understanding of the initial state of a building using a calibrated prediction model. Sev-eral works have investigated the use of identification techniques for model calibration.

Given the dominating impact of the built environment on global carbon emissions, reducing operational energy use in buildings has long been considered a key strategy towards more sustainable urban development. In contrast, building energy use related to material production, construction, and demolition has been considered to be significantly less important. However, given the proliferation of low and even net zero energy buildings, more holistic life cycle assessment (LCA) of buildings becomes necessary.

This paper presents a building energy simulation study of the impact of electrical peak demand reduction strategies in a large office tower in New York that has extensive use of daylight responsive dimmable ballasts, and under floor air distribution systems (UFAD). The complexity and large size of the building imposed careful simplification of the simulated geometry in order to make the simulations and results analysis manageable.

Towards the achievement of Nearly-Zero Energy Buildings (nZEB), the call for high performance Building Systems (BS) is undeniable. In order to provide, control and reduce the energy used by the BS, complex and sophisticated technologies are more and more introduced. This complex scenario requires computer simulation to evaluate the building performance at design time. To reach this goal, a Building Performance Simulation Tool (BPST) should carefully consider the accuracy of each component’s input data and the sensitivity of the simulation results to these uncertainties.

Building recommissioning in essential in the aging building stock to maintain efficient and comfortable operation as equipment ages and portions of the build-ing are re-purposed for uses other than what was orig-inally intended. Model-based recommissioning pro-vides a way to evaluate payback and incentives for equipment replacement, and the response of the build-ing to optimized operational strategies - without dis-turbing the comfort or productivity of current occu-pants. Accurate models are needed for these investi-gations, which must be calibrated to available sensor data.

This research proposes a Bayesian approach to include uncertainty that arises from modeling process and input values when predicting cooling and heating consumption in existing buildings. Our approach features Gaussian Process modeling. We present a case study of predicting energy use through a Gaussian Process and compare its accuracy with a Neural Network model. As an initial step of applying Gaussian Processes to uncertainty analysis of system operations, we evaluate the impact of uncertain air-handling unit (AHU) supply air temperature on energy consumption.

In Egypt, the current widely used external walls are preferred in practice to minimize the project's initial cost, regardless of the negative impacts of the thermal comfort on the inhabitants, as well as the impact on the running cost of the energy consumption later.  The objective of this research is to evaluate the effect of external walls with different specifications on the project’s initial cost and running cost for achieving internal thermal comfort in the present time and under climate change.