<div dir="ltr"><p class="">Join us
<b>Tuesday, March 19</b> for the next meeting of the Rocky Mountain
Energy Simulation Engineers (RMESE). Five masters and PHD candidates
from the University of Colorado will have 15 minutes each to present on
their respective energy modeling projects for their
research. The presenters/presentations for the evening will be as
follows:</p>
<p class="" style="margin-left:0.5in"><b>Neal Kruis</b></p>
<p class="" style="margin-left:0.5in">How do you account for
heat loss from your building foundation? Many energy modelers either
drastically simplify this heat loss in their models or neglect it
entirely. This is because the multi-dimensional, large-timescale
nature of foundation heat losses can become very computationally
intensive, to the point where it becomes time prohibitive for most
modeling projects.</p>
<p class="" style="margin-left:0.5in">My research aims at answering several unanswered questions about foundation heat loss: </p>
<p class="" style="margin-left:1in">
<span style="font-size:10pt;font-family:Symbol"><span>·<span style="font:7pt "Times New Roman"">
</span></span></span>How significant is foundation heat loss relative to other loads in the building? </p>
<p class="" style="margin-left:1in">
<span style="font-size:10pt;font-family:Symbol"><span>·<span style="font:7pt "Times New Roman"">
</span></span></span>How good are our current tools at estimating these losses? </p>
<p class="" style="margin-left:1in">
<span style="font-size:10pt;font-family:Symbol"><span>·<span style="font:7pt "Times New Roman"">
</span></span></span>What can be done to improve theses estimations and reduce computation time? </p>
<p class="" style="margin-left:1in">
<span style="font-size:10pt;font-family:Symbol"><span>·<span style="font:7pt "Times New Roman"">
</span></span></span>What are best practices for insulating building foundations?</p>
<p class="" style="margin-left:0.5in"><b>Anna Osborne</b></p>
<p class="" style="margin-left:0.5in">A grant awarded to the
International Center for Appropriate and Sustainable Technology (ICAST)
from the U.S. Department of Housing and Urban Development (HUD) is
funding a study to assess the effect of energy conservation
measures (ECMs) and behavioral change measures (BCMs) on the energy use
patterns of 800 units of multi-family low-income residential
properties. Since the effects of ECMs are known and calculable, the main
goal of the study is to determine the degree of change
that can be achieved through education of the occupants and combined
effects of ECMs and BCMs. Anna has also developed a Matlab program which
performs complex linear regression on utility data to assess the degree
of efficiency of the building in question.
The energy modeling component involves a sensitivity study on occupant
behavior by adjusting schedules and setpoints as well as comparison to
the Matlab program. The results are still preliminary. </p>
<p class="" style="margin-left:0.5in"><b> </b></p>
<p class="" style="margin-left:0.5in"><b>Ben Brannon</b></p>
<p class="" style="margin-left:0.5in">Ben Brannon will be
presenting the initial development of his thesis on the Modeling and
Control Design and Field Experimentation of a Thermally Activated
Residence. The project stems from a home in Missouri that
attempts to direct heat obtained from massy walls to beneficial
locations around the home and control radiative heat transfer.</p>
<p class="" style="margin-left:0.5in"> </p>
<p class="" style="margin-left:0.5in"><b>Lincoln Harmer</b></p>
<p class="" style="margin-left:0.5in">This project serves as a
proof-of-concept that calibrated building models when applied real-time
information provide enhanced energy management and diagnostic
capabilities. Rather than comparing against a static
baseline as is customary in Monitoring Based Comissioning (MBCx), the
building models used in this project are dynamically kept in tune with
the actual building, over time horizons stretching several time scales. A
straightforward energy management application
involves determining the percent deviation in facility natural gas
consumption or electricity use between the actual building and its model
over the last day, week, month, and year; a predefined threshold of
permissible deviation (say +/- 10 ) would then trigger
an alarm or alert to the building management staff. Selecting the right
performance metrics will be part of this research. Envisioned examples
of extended diagnostic capabilities include a) fault detection and
diagnosis (FDD) such as finding the most likely
HVAC system parameters that explain the observed consumption patterns
and thus detecting system degradation, b) prediction of facility energy
use and electrical demand for the next day or week, and c) load
aggregation for multiple buildings to be served by
an electrical supplier in a deregulated utility context. The simulation
models employed may be white box models based on first principles,
statistical black box models using only monitored data, or inverse gray
box models that combine reduced order building
physics with simplified models for the energy systems. These inverse
gray box models involving both first principles as well as parameter
estimation appear as likely contenders. The expected result is a
software demonstration of using a building model and
measured historical and/or real-time building information for building
performance evaluation. While many of the research questions may be
answered using surrogate data, we’re collecting real-time data from two
facilities and are aiming to prove the concept
on both buildings using a real-time field implementation. </p>
<p class="" style="margin-left:0.5in"> </p>
<p class="" style="margin-left:0.5in"><b>Ryan Tanner</b></p>
<p class="" style="margin-left:0.5in">My research is entitled <i>Stochastic Optimal Control of Mixed Mode Buildings Considering Occupant Driven Uncertainty</i>. </p>
<p class="" style="margin-left:0.5in">
I use offline model predictive control to optimize the controls of
(simulated) mixed mode buildings, a process which results in 'optimal
control datasets'. Next, I use machine-learning algorithms to derive
viable BAS control rules from the optimal control datasets.
EnergyPlus has been the building energy modeling tool used throughout
my research, providing virtual test-buildings to try different controls
in. Throughout all simulations, occupant behavior is accounted for by
one of two methods. (1) Co-simulation using
the building controls virtual test bed (BCVTB) to couple the EnergyPlus
simulation with occupant behavior algorithms written in matlab. (2)
Writing occupant behavior algorithms directly into EnergyPlus via the
Energy Management System (EMS).</p>
<p>This will be an exciting group of presentations, so make sure you join us to support our future modelers!!!</p>
<p>We will be meeting at <b><span style="background:none repeat scroll 0% 0% yellow">The Tavern Downtown (1949 Market Street, Denver, CO 80202)</span> at 6:00 pm Tuesday, March 19
</b>for<b> </b>the five short presentations and drinks. </p>
<p>Please RSVP to Jessie Jones at <a href="mailto:jjones@rmhgroup.com" target="_blank">
jjones@rmhgroup.com</a> or <a href="tel:%28303%29312-4641" target="_blank">(303)312-4641</a>
so that we can make sure our reservation is large enough. This is not
invite only- so pass the word on to coworkers, colleagues, and other
industry professionals who
may be interested.</p>
<p class="">Thank you,</p>
<b><span style="font-size:10.5pt;color:rgb(31,73,125)">RMESE Steering Committee<br><br></span></b></div>