Towards Robust Energy Demand Response At Airports

Abstract

Airports have a great motivation for taking advantage of demand response (DR) opportunities considering their large energy footprint and continuous operation. A robust energy baseline model, which calculatates what the power demand would have been without any curltailment, is crucial to realize this motivation as it assesses the DR potential and the effectiveness of DR strategies. Since such baseline models are specific to building types and operational characteristics, this study targets developing an airport-specific energy baseline model to help airport operators utilize DR opportunities. For the purpose, first we perform visual inspection to analyze the relationships between the power demand and explanatory variables, such as time-of-day, time-of-week, outside temperature, and the number of passengers of departure flight and arrival flights. Then, we develop airport-specific energy baseline models through linear regression analysis with ten different combinations of explanatory variables. Finally, we analyze the regression coefficients of each model to understand the impact of variables on the airport power demand. The results show the model with time-of-week and outside temperature has the lowest mean absolute percentage error (MAPE) of 2.72% (305.87 kW) and using time-of-week rather than time-of-day reduce the error by about 4.1 ~ 4.8 kW. It is also found that both departure and arrival flight schedules do not significantly increase the prediction accuracy. Through coefficients analysis, we also find the isolated impact of each variable on the airport power demand, which inform airport operators about the contribution of each variable, such as flight schedule, to the whole airport power demand.