Improving the Energy Management System with the Interval Type-2 Fuzzy Inference System – Zebra Optimization Algorithm (IT2FIS-ZOA) for Predicting the Load Consumption of Healthcare Facilities in National Holiday Season - Ramadhani, Akhmad; Robandi, Imam; Djalal, Muhammad Ruswandi; Prakasa, Mohamad Almas (Engineering, Technology & Applied Science Research, 2025)
The improvement of Energy Management System (EMS) with a load consumption prediction feature is essential to the uninterrupted operation of medical equipment in healthcare facilities, such as hospitals. Accurately predicting their energy consumption demands leads to effective energy management. Additionally, the load consumption prediction is essential to achieve the energy-saving goals in the construction sector. This paper aims to improve the EMS of the Ulin Regional Public Hospital, one of the largest hospitals in South Kalimantan of Indonesia, by utilizing the Interval Type-2 Fuzzy Inference System – Zebra Optimization Algorithm (IT2FIS-ZOA) to predict the load consumption during the national holiday season. The IT2FIS-ZOA will enhance the exploration and exploitation processes to provide more accurate results. The load demand prediction is conducted based on the historical data of the Ulin Regional Public Hospital during 14 previous national holidays between 2020 and 2022. The accuracy of IT2FIS-ZOA is validated through a comparison of the prediction with actual data from 2023. Furthermore, IT2FIS-ZOA performance is compared to the Big Bang-Big Crunch Algorithm (BBBC), Firefly Algorithm (FA), and Cuckoo Search Algorithm (CSA). The findings indicate that the most accurate load consumption prediction is obtained from IT2FIS-ZOA with the lowest Mean Absolute Percentage Error (MAPE) of 2.49%, compared to a Fuzzy Type-1 of 2.74%, Fuzzy Type-2 of 2.55%, IT2FIS-BBBC of 3.91% , IT2FIS-FA of 2.51%, and IT2FIS-CSA of 2.50%. The results demonstrate the superiority of IT2FIS-ZOA in improving the EMS of the Ulin Regional Public Hospital.