Abstract
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.
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