Airport Electricity Consumption Demand Forecasting Model using Seasonal Autoregressive Integrated Moving Average.

Electric load forecasting, also known as Probabilistic Load Forecasting (PLF), has played a role in the electric power industry. Forecasting the electricity consumption in business is necessary for planning power system operations, stability, and energy trading. Many business entities, such as commercial airports, require electric load forecasting to meet service and regulatory needs. Therefore, forecasting is needed to become a reference in determining strategic management energy. This research aims to forecast the electricity consumption of Soekarno-Hatta International Airport using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The study uses daily historical data collected from airport operator companies from 01 January 2022 to 31 December 2022 to build and evaluate the model's performance. The findings show that the SARIMA model (1,1,1)(0,1,1)⁷ has the best model accuracy with a MAPE of 4.62%. The study conclusions highlight the potential of the model to support energy management practices at Soekarno Hatta International Airport and other similar facilities. [ABSTRACT FROM AUTHOR]