11:15am - 11:18am
Energy use of buildings in relation to occupancy patterns
1Lund University, Sweden; 2Skanska Sverige AB, Sweden
“Buildings don’t use energy, people do” recited the title of a famous publication. In ideal circumstances, and for the same weather conditions, there should be a proportionality between the istantaneous energy use of a building and its occupancy. Presence sensors, for example, are a strategy to adapt energy use to occupancy, as they allow an active control of lighting, temperature settings and ventilation flows. However, only part of the total energy use for building can be controlled by presence sensors, and the savings might be limited. In a similar way, schedules, for example lowering the set-point temperature for the heating in a building during non-working hours, can be effective but they might not respond promptly to unexpected changed in occupancy patterns. In practice, empty buildings will still use some energy, even when people do not.
During the ongoing Covid 19 outbreak many offices have exceptionally low occupancy as many of the ordinary users are rather working from their homes. This offers a unique opportunity to evaluate, for example, the benefits with using presence sensors in office buildings, or how much a building can adapt to occupancy patterns. We retrieved annual energy and occupancy data from energy-efficient retrofitted office buildings in Sweden for the years 2019 and 2020 and we are analysing them to evaluate the energy use before and during the Covid 19 outbreak.
The data are currently being organized. We hypothesize that there is little proportionality between occupancy and different building services, where a large part of energy use is indepent from occupancy. As a consequence, we expect that energy-efficient buildings are little adaptable to change in occupancy patterns. This would suggest that systems performance should be optimized also for non-occupancy hours, while much is currently investigated on maximizing system performances during occupied hours.
11:18am - 11:21am
Community cooling infrastructure from waste heat among diverse building types in Rourkela Steel Township, India
1NIT Rourkela, India; 2Princeton University, School of Architecture & the Andlinger Center for Energy and the Environment, Princeton, New Jersey, 08544, USA; 3AIL Research and Princeton University, Andlinger Center for Energy and the Environment, Princeton, New Jersey, 08544, USA
Urban building energy modeling is an important field in the current decade due to the rising rate of urbanization, specifically in developing countries. The UN environment is promoting urban level space cooling approaches in the upcoming smart cities of India. Rourkela is a tier-2 steel township included within the ‘smart city’ mission in India and houses one of the largest Steel Plants of India, classified under Koppen Aw tropical climate zone. However it experiences extreme heat stress in the dry summer season before the onset of monsoons. The given study proposes an alternative cooling scenario utilizing waste heat from the rolling mill with which cooling in the range of 700-900 tons of nearly zero energy cooling can be made available in the surrounding areas, otherwise catered by an energy intensive cooling system reporting a COP of 2.45. This study can be further expanded to provide cooling to the nearby residential communities keeping the steel plant area as center point for community cooling infrastructure provision.
11:21am - 11:24am
Statistical analyses of the energy demand and thermal comfort for multiple uncertain input parameters performed using transformed variable and perturbation method.
Lodz University of Technology, Poland
In the calculations of the buildings’ energy demand, the input parameters are usually considered as strictly determined values. Meanwhile, numerous of them may be characterized by certain probability density functions and thus propagation of such uncertainties in the calculations should be also analyzed. In the energy-demand related problems, the uncertainty analyses is usually performed using the Monte Carlo method. However, this method requires multiple calculations and, therefore, may be very time-consuming.
In the proposed work, three approaches are applied for the probabilistic studies: the stochastic perturbation method, the transformed random variables method and for comparison the Monte Carlo method. Results of both the energy demand and the thermal comfort are analyzed.
The stochastic analysis is based on the response functions and their derivatives with respect to all random input parameters. The relation between the energy demand, the thermal comfort and the input random variable have been calculated using the EnergyPlus software. Afterwards, the response functions were estimated using the polynomial regression analyses. Using this method, the expected value and central moments of the response functions were calculated by means of perturbation method. Meanwhile, the transformed random variables method allowed to obtain, using the same response functions, the implicit form of probability distributions function of the output parameter. Based on the probability density functions any statistical parameter might be calculated.
The obtained data was compared against the Monte Carlo method results and good accordance has been reviled. The number of Monte Carlo simulations had to be very high to provide robust results, especially for nonlinear relations. The presented methods allowed to obtain quickly the distribution of the response functions in the energy demand and the thermal comfort analyses and may be successfully applied in the software, allowing to account for uncertainties in the input parameters.
11:24am - 11:27am
Primary energy efficiency assessment of a coil heat recovery system within the air handling unit of an operating room
Universidad de Valladolid, Spain
Heat recovery systems installed in Air Handling Units (AHUs) are energy efficient solutions during disparate outdoor-to-indoor temperatures. However, they may be detrimental in terms of a primary energy balance when these temperatures get closer, due to the decrease in the thermal energy recovered compared to the global energy consumption required for their operation.
AHUs in surgical areas have certain particularities such as their continuous operation throughout the year, the large airflows supplied and the strict exigencies on the supply air quality, avoiding any cross contamination. This work presents the measurements and analysis performed on a coil heat recovery (run-around) loop system installed in the AHU that serves a mixed-air ventilation operating room in a Hospital Complex.
A primary energy balance is studied, including the thermal and electric energy savings achieved, considering the electric energy consumption by the recirculation pump and the additional power requirements of fans due to the pressure drop introduced. The obtained value is then used to predict the thermal energy savings achieved by the heat recovery system. Results are extrapolated to the Typical Meteorological Year to provide an order of magnitude of the primary energy and CO2 emissions saved through the operation of the coil heat recovery system.
11:27am - 11:30am
Develop a New Approach to Evaluate Energy Savings, Thermal Comfort and IAQ from Occupant-Centric Building Controls
Syracuse University, United States of America
Occupant behavior is identified as one of the key factors influencing the performance of building systems. Various studies have shown that occupant behavior such as switching on and off the lights, turning on and off the heating, ventilation, and air-conditioning (HVAC) system, and adjusting the thermostat can significantly affect the energy use and indoor environmental quality of the building. Occupancy-centric control is famous for its potential to save building energy without sacrificing occupants’ comfort. This study utilized two identical lab spaces, configured as typical open-plan offices, to investigate the performance of the occupancy-centric control in terms of energy-saving, indoor air quality, and thermal comfort. The occupancy profile measured by an occupancy counting sensor was mapped into these two rooms by using heating bulbs and CO2 to mimic occupants’ load. The results have demonstrated that occupancy-centric control could save around 28% total energy, including fan, cooling, and heating energy, with minimal impact on the air quality and thermal comfort.