Methodology to Develop the Airport Terminal Building Energy Use Intensity (ATB-EUI) Benchmarking Tool

Haberl, J. S.; Zilbershtein, G.; Baltazar, Juan-Carlos; Mao, Chongyang; Ugursal, Assiya; Nelson, I.; Parker, Patrick L.; Yazdani, B.; Martinez, Joseph T; Cho, Soolyeon; Stratakes, Travis; Jiang, Anjie; Dong, Lining; Hussain, Marshall

doi:10.17226/23495

Cited by 1 publication

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“…Moreover, the power data used in this study covered the electricity demand not only of the airside building but of the whole facility, including parking lot and hotel, which made occupant impact on total demand more negligible. Therefore, a true investigation of the impact of passengers on airport energy demand would require further investigation of other airports in different climate regions and of different sizes because the energy use intensities and end use patterns of those airports are different from those for PIT (22). Second, the assumption about occupancy does not hold in real practice.…”

Section: Resultsmentioning

confidence: 99%

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Forecasting Airport Building Electricity Demand on the Basis of Flight Schedule Information for Demand Response Applications

Kang

Bergés

Akinci

2017

Transportation Research Record: Journal of the Transportation R

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Airports are poised to take advantage of demand response (DR) opportunities because of their large energy footprint and continuous operations. To develop an energy baseline model (i.e., the estimate of the expected load without curtailment), airports need special attention because of their continually changing operations and occupant levels, which result from varying flight schedules. However, an accurate baseline is also important for determining fair incentives and assessing DR strategies. This study, therefore, aimed to develop airport-specific energy baseline models by incorporating flight departure and arrival information. Therefore, the paper first analyzes relationships between airport power demand and potential predictors, such as time of day, time of week, outside temperature, and number of passengers on departing and arriving flights at a case study airport. Second, it develops piecewise linear regression models with combinations of variables and compares the models' prediction performance. The results show that the model with time of week and outside temperature had the lowest mean absolute percentage error, 2.72% (305.87 kW). However, schedules of neither departing nor arriving flights significantly increased prediction accuracy, contrary to the initial assumption because, for the case study airport, the influx and outflow of occupants had little impact on whole-airport energy consumption compared with consumption from regular operations. Hence, to understand the true value of a flight schedule in relation to airport power demand, investigation of airports with different sizes and climate zones is required. However, the method suggested here for understanding airport energy consumption and developing airport-specific energy baselines still holds and can be applied in universal cases.Modernization of the power grid has brought numerous opportunities for buildings to participate in grid-level services (e.g., frequency control, operating reserves, etc.) through demand response (DR) programs. These programs incentivize customers to curtail their power usage during peak demand period and have entranced many building owners not only as an energy cost-saving opportunity but also as a chance to serve their communities by stabilizing the local grids and reducing the environmental impacts of running additional power plants. A recent ruling by the U.S. Supreme Court to support DR programs is also expected to secure DR benefits further and to expedite its implementations (1). Although airports have a good starting point for discovering such DR opportunities, because of their large energy footprint and continuous operations, airport facility managers have had difficulty tapping into such opportunities because of the complexity of the building systems and the uncertainties associated with occupant comfort.One important step toward an airport's use of DR opportunities is having an accurate energy baseline model that describes the airport's energy consumption profiles. Such a model is important for two main reasons:...

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Section: Resultsmentioning

confidence: 99%

“…Temperature Component sizes because the energy use intensities and end use patterns of those airports are different from those for PIT (22). Second, the assumption about occupancy does not hold in real practice.…”

Section: Scenario Time Componentmentioning

confidence: 99%