Accurate forecasting is essential for effective energy management, particularly in evolving and data-driven electricity markets. To address the increasing complexity of national energy planning in The Netherlands, this study proposes a hybrid multi-stage forecasting framework to improve both short- and long-term electricity demand predictions. We compare three model types, classical statistical (SARIMAX), hybrid statistical–deep learning (SARIMAX–LSTM), and deep learning (sequence-to-sequence), across forecasting horizons from 1 to 180 days. The models are trained on daily load data from ENTSO-E (2009–2023), incorporating exogenous variables such as weather conditions, energy prices, and socioeconomic indicators, as well as engineered temporal features such as calendar effects, seasonal patterns, and rolling demand statistics. Three feature configurations were tested: exogenous-only, generated-only, and a combined set. Internally generated features consistently outperformed exogenous inputs, especially for long-term forecasts. The sequence-to-sequence model achieved the highest accuracy at the 180-day horizon, with a mean absolute percentage error (MAPE) of approximately 1.88%, outperforming both SARIMAX and the SARIMAX–LSTM hybrid models. An additional SARIMAX-based analysis assessed the individual effects of renewable and socioeconomic indicators. Renewable energy production improved short-term accuracy (MAPE reduced from 2.13% to 1.09%) but contributed little to long-term forecasting. Socioeconomic variables had limited predictive value and, in some cases, slightly reduced accuracy, particularly over long-term horizons.

Discretionary activities such as retail, food, and beverages generate a significant amount of non-aeronautical revenue within the aviation industry. However, they are rarely taken into account in computational airport terminal models. Since discretionary activities affect passenger flow and global airport terminal performance, discretionary activities need to be studied in detail. Additionally, discretionary activities are influenced by other airport terminal processes, such as check-in and security. Thus, discretionary activities need to be studied in relation to other airport terminal processes. The aim of this study is to analyze discretionary activities in a systemic way, taking into account interdependencies with other airport terminal processes and operational strategies used to manage these processes. An agent-based simulation model for airport terminal operations was developed, which covers the main handling processes and passenger decision-making with discretionary activities. The obtained simulation results show that operational strategies that reduce passenger queue time or increase passenger free time can significantly improve global airport terminal performance through efficiency, revenue, and cost.