Abstract: Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.
Abstract: In nearly all earthquakes of the past century that
resulted in moderate to significant damage, the occurrence of postearthquake
fire ignition (PEFI) has imposed a serious hazard and
caused severe damage, especially in urban areas. In order to reduce
the loss of life and property caused by post-earthquake fires, there is
a crucial need for predictive models to estimate the PEFI risk. The
parameters affecting PEFI risk can be categorized as: 1) factors
influencing fire ignition in normal (non-earthquake) condition,
including floor area, building category, ignitability, type of appliance,
and prevention devices, and 2) earthquake related factors contributing
to the PEFI risk, including building vulnerability and earthquake
characteristics such as intensity, peak ground acceleration, and peak
ground velocity. State-of-the-art statistical PEFI risk models are
solely based on limited available earthquake data, and therefore they
cannot predict the PEFI risk for areas with insufficient earthquake
records since such records are needed in estimating the PEFI model
parameters. In this paper, the correlation between normal condition
ignition risk, peak ground acceleration, and PEFI risk is examined in
an effort to offer a means for predicting post-earthquake ignition
events. An illustrative example is presented to demonstrate how such
correlation can be employed in a seismic area to predict PEFI hazard.
Abstract: During an earthquake, a bridge crane may be
subjected to multiple impacts between crane wheels and rail. In order
to model such phenomena, a time-history dynamic analysis with a
multi-scale approach is performed. The high frequency aspect of the
impacts between wheels and rails is taken into account by a Lagrange
explicit event-capturing algorithm based on a velocity-impulse
formulation to resolve contacts and impacts. An implicit temporal
scheme is used for the rest of the structure. The numerical coupling
between the implicit and the explicit schemes is achieved with a
heterogeneous asynchronous time-integrator.