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
Received: 10 May 2025 | Revised: 4 June 2025 and 21 June 2025 | Accepted: 1 July 2025 | Online: 2 August 2025
Corresponding author: Imam Robandi
Abstract
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.
Keywords:
energy management system, load consumption prediction, interval type-2 fuzzy inference system, zebra optimization algorithmDownloads
References
Q. Hassan et al., "The renewable energy role in the global energy Transformations," Renewable Energy Focus, vol. 48, Mar. 2024, Art. no. 100545. DOI: https://doi.org/10.1016/j.ref.2024.100545
P. Singh, S. Singh, G. Kumar, and P. Baweja, Energy: Crises, Challenges and Solutions. NJ, USA: John Wiley & Sons, Inc., 2021. DOI: https://doi.org/10.1002/9781119741503
F. Firman, N. H. Said, and M. R. Djalal, "Characteristic Analysis of Solar Panels on Clay and Ceramic Roof Tiles," International Review of Mechanical Engineering, vol. 16, no. 3, pp. 130–138, Mar. 2022. DOI: https://doi.org/10.15866/ireme.v16i3.20004
J. L. Holechek, H. M. E. Geli, M. N. Sawalhah, and R. Valdez, "A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050?," Sustainability, vol. 14, no. 8, Apr. 2022, Art. no. 4792. DOI: https://doi.org/10.3390/su14084792
J. Jamal and M. R. Djalal, "Effect of Outlet at the Bottom of the Drying Chamber on the Performance of Natural Convection Solar Dryers," Przeglad Elektrotechniczny, no. 11, Nov. 2023. DOI: https://doi.org/10.15199/48.2023.11.26
S. M. Amin Mirjalili, A. Aslani, and R. Zahedi, " Towards Sustainable Commercial-Office Buildings: Harnessing the Power of Solar Panels, Electric Vehicles, and Smart Charging for Enhanced Energy Efficiency and Environmental Responsibility," Case Studies in Thermal Engineering, vol. 52, Dec. 2023, Art. no. 103696. DOI: https://doi.org/10.1016/j.csite.2023.103696
S. Chapaloglou et al., "Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system," Applied Energy, vol. 238, pp. 627–642, Mar. 2019. DOI: https://doi.org/10.1016/j.apenergy.2019.01.102
S. M. Al-azzawi, M. A. Deif, H. Attar, A. Amer, and A. A. A. Solyman, "Hyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Period," International Journal of Intelligent Engineering and Systems, vol. 16, no. 4, 2023. DOI: https://doi.org/10.22266/ijies2023.0831.20
W. Aribowo, "Optimizing Feed Forward Backpropagation Neural Network Based on Teaching-Learning-Based Optimization Algorithm for Long-Term Electricity Forecasting," International Journal of Intelligent Engineering and Systems, vol. 15, no. 1, 2022. DOI: https://doi.org/10.22266/ijies2022.0228.02
M. A. Hammad, B. Jereb, B. Rosi, and D. Dragan, "Methods and Models for Electric Load Forecasting: A Comprehensive Review," Logistics, Supply Chain, Sustainability and Global Challenges, vol. 11, no. 1, pp. 51–76, Feb. 2020. DOI: https://doi.org/10.2478/jlst-2020-0004
S. Aslam, H. Herodotou, S. M. Mohsin, N. Javaid, N. Ashraf, and S. Aslam, "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, vol. 144, 2021. DOI: https://doi.org/10.1016/j.rser.2021.110992
A. Gasparin, S. Lukovic, and C. Alippi, "Deep learning for time series forecasting: The electric load case," CAAI Transactions on Intelligence Technology, vol. 7, no. 1, pp. 1–25, Nov. 2022. DOI: https://doi.org/10.1049/cit2.12060
H. J. Sadaei, P. C. de Lima e Silva, F. G. Guimarães, and M. H. Lee, "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, vol. 175, pp. 365–377, 2019. DOI: https://doi.org/10.1016/j.energy.2019.03.081
M. Madhukumar, A. Sebastian, X. Liang, M. Jamil, and M. N. S. K. Shabbir, "Regression Model-Based Short-Term Load Forecasting for University Campus Load," IEEE Access, vol. 10, pp. 8891–8905, Jan. 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3144206
G.-C. Lee, "Regression-Based Methods for Daily Peak Load Forecasting in South Korea," Sustainability, vol. 14, no. 7, Mar. 2022, Art. no. 3984. DOI: https://doi.org/10.3390/su14073984
S. Sharma, A. Majumdar, V. Elvira, and E. Chouzenoux, "Blind Kalman Filtering for Short-term Load Forecasting," IEEE Transactions on Power Systems, vol. 35, no. 6, pp. 4916–4919, Nov. 2020. DOI: https://doi.org/10.1109/TPWRS.2020.3018623
Z. Zheng, H. Chen, and X. Luo, "A Kalman filter-based bottom-up approach for household short-term load forecast," Applied Energy, vol. 250, pp. 882–894, Sep. 2019. DOI: https://doi.org/10.1016/j.apenergy.2019.05.102
M. A. Prakasa, I. Robandi, A. Borghetti, M. R. Djalal, and W. Himawari, "Coordinated Design of Power System Stabilizer and Virtual Inertia Control Using Modified Harris Hawk Optimization for Improving Power System Stability," IEEE Access, vol. 13, pp. 2581–2603, 2025. DOI: https://doi.org/10.1109/ACCESS.2024.3522291
I. Robandi et al., "Stability Improvement of Sulbagsel Electricity System Integrated Wind Power Plant Using SVC-PSS3C Based on Improved Mayfly Algorithm," Results in Engineering, vol. 24, Dec. 2024, Art. no. 103407. DOI: https://doi.org/10.1016/j.rineng.2024.103407
A. Divasson-J et al., "Analysis of the applicability and results of swarm intelligence tools for the positioning of Energy Storage Systems," International Journal of Electrical Power and Energy Systems, vol. 163, 2024. DOI: https://doi.org/10.1016/j.ijepes.2024.110343
R. N. Hasanah et al., "A Novel Horse Herd Optimization Algorithm for Optimal Economic Dispatch in Sulbagsel Electricity System," International Journal of Intelligent Engineering and Systems, vol. 17, no. 6, pp. 1059–1069, 2024. DOI: https://doi.org/10.22266/ijies2024.1231.78
Syafaruddin et al., "Short-Term Peak Load Forecasting Using Interval Type-2 Fuzzy Logic - Horse Herd Optimization Algorithm in Sulbagsel Electricity, System," International Journal of Intelligent Engineering and Systems, vol. 18, no. 1, pp. 268–278, 2025. DOI: https://doi.org/10.22266/ijies2025.0229.19
S. Zairi and M. Freihat, "Electric Load Forecasting using Machine Learning for Peak Demand Management in Smart Grids," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23335–23346, Jun. 2025. DOI: https://doi.org/10.48084/etasr.10687
A. Ramadhani, I. Robandi, M. R. Djalal, and M. A. Prakasa, "Modeling of Power Management Systems for Healtcare Facilities Using Hybrid Fuzzy-Particle Swarm Optimization: 25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024," 2024 International Seminar on Intelligent Technology and Its Applications, pp. 424–429, 2024. DOI: https://doi.org/10.1109/ISITIA63062.2024.10667879
A. K. De, D. Chakraborty, and A. Biswas, "Literature review on type-2 fuzzy set theory," Soft Comput., vol. 26, no. 18, pp. 9049–9068, Nov. 2022. DOI: https://doi.org/10.1007/s00500-022-07304-4
N. N. Karnik and J. M. Mendel, "Introduction to type-2 fuzzy logic systems," in Proceedings of IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence, Anchorage, AK, USA, Feb. 1998, vol. 2, pp. 915–920. DOI: https://doi.org/10.1109/FUZZY.1998.686240
Y. Chen and D. Wang, "Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms," Transactions of the Institute of Measurement and Control, vol. 41, no. 10, pp. 2886–2896, Jun. 2019. DOI: https://doi.org/10.1177/0142331218816753
J. Jamaaluddin et al., "Very Sort Term Load Forecasting Using Interval Type - 2 Fuzzy Inference System (IT- 2 FIS) (Case Study: Java Bali Electrical System)," in IOP Conference Series: Materials Science and Engineering, Bandung, Indonesia, Apr. 2018, vol. 384, Art. no. 012078. DOI: https://doi.org/10.1088/1757-899X/384/1/012078
Syafaruddin, "Interval Type Two Fuzzy Logic System (IT2FLS) Based Short-Term Load Forecasting," ICIC Express Letters, vol. 9, no. 9, pp. 2445–2452, Nov. 2015.
S. Lidelöw, T. Örn, A. Luciani, and A. Rizzo, "Energy-efficiency measures for heritage buildings: A literature review," Sustainable Cities and Society, vol. 45, pp. 231–242, Feb. 2019. DOI: https://doi.org/10.1016/j.scs.2018.09.029
L. Cao, Y. Li, J. Zhang, Y. Jiang, Y. Han, and J. Wei, "Electrical load prediction of healthcare buildings through single and ensemble learning," Energy Reports, vol. 6, pp. 2751–2767, Nov. 2020. DOI: https://doi.org/10.1016/j.egyr.2020.10.005
I. Soyler and E. Izgi, "Electricity Demand Forecasting of Hospital Buildings in Istanbul," Sustainability, vol. 14, no. 13, Jul. 2022, Art. no. 8187. DOI: https://doi.org/10.3390/su14138187
S. Chaaraoui et al., "Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms," Energies, vol. 14, no. 2, Jan. 2021, Art. no. 409. DOI: https://doi.org/10.3390/en14020409
Z. Janković, S. Ilić, B. Vesin, and A. Selakov, "Special Day Regression Model for Short-Term Load Forecasting," in Proceedings of PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Novi Sad, Serbia, Jul. 2022, pp. 1–5. DOI: https://doi.org/10.1109/ISGT-Europe54678.2022.9960317
F. ZIEL, "Modeling public holidays in load forecasting: a German case study," Journal of Modern Power Systems and Clean Energy, vol. 6, no. 2, pp. 191–207, Feb. 2018. DOI: https://doi.org/10.1007/s40565-018-0385-5
M. López, C. Sans, S. Valero, and C. Senabre, "Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study," Energies, vol. 12, no. 7, Apr. 2019, Art. no. 1253. DOI: https://doi.org/10.3390/en12071253
A. Huang, J. Zhou, T. Cheng, X. He, J. Lv, and M. Ding, "Short-term load forecasting for holidays based on similar days selecting and XGBoost model," in Proceedings of 6th International Conference on Industrial Cyber-Physical Systems, Wuhan, China, Feb. 2023, pp. 1–6. DOI: https://doi.org/10.1109/ICPS58381.2023.10128055
J. Son, J. Cha, H. Kim, and Y.-M. Wi, "Day-Ahead Short-Term Load Forecasting for Holidays Based on Modification of Similar Days’ Load Profiles," IEEE, vol. 10, pp. 17864–17880, Feb. 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3150344
H. Dong, Y. Gao, Y. Fang, M. Liu, and Y. Kong, "The Short-Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China," Computational Intelligence and Neuroscience, no. 1, Sep. 2021, Art. no. 3693294. DOI: https://doi.org/10.1155/2021/3693294
Q. Ren and F. Feng, "PID parameter tuning optimization based on multi-strategy fusion improved zebra optimization algorithm," The Journal of Supercomputing, vol. 81, no. 1, Dec. 2024, Art. no. 266. DOI: https://doi.org/10.1007/s11227-024-06548-1
E. Trojovská, M. Dehghani, and P. Trojovský, "Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm," IEEE Access, vol. 10, pp. 49445–49473, May 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3172789
Y. Sun, C. Wu, M. Cong, and B. Yan, "Research and application of improved Zebra optimization algorithm," in Proceedings of 5th International Conference on Computer Engineering and Application, Hangzhou, China, Apr. 2024, pp. 203–27. DOI: https://doi.org/10.1109/ICCEA62105.2024.10603809
F. Abdelmalek et al., "Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems," Scientific Reports, vol. 14, no. 1, Oct. 2024, Art. no. 26047. DOI: https://doi.org/10.1038/s41598-024-77488-2
Z. Qi, S. Peng, P. Wu, and M.-L. Tseng, "Renewable Energy Distributed Energy System Optimal Configuration and Performance Analysis: Improved Zebra Optimization Algorithm," Sustainability, vol. 16, no. 12, Jun. 2024, Art. no. 5016. DOI: https://doi.org/10.3390/su16125016
A. Ramadhani, A. Dharma, and I. Robandi, "Optimization FOU of Interval Type-2 Fuzzy Inference System Using Big Bang – Big Crunch Algorithm for Short Term Load Forecasting on National Holiday Case Study: South and Central Kalimantan-Indonesia," International Review of Electrical Engineering, vol. 10, no. 1, pp. 123–130, Jan. 2015. DOI: https://doi.org/10.15866/iree.v10i1.4871
Downloads
How to Cite
License
Copyright (c) 2025 Akhmad Ramadhani, Imam Robandi, Muhammad Ruswandi Djalal, Mohamad Almas Prakasa
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
