Empirical Models for Forecasting Power Backup Duration: A Case Study on Telecommunication Systems
Keywords:
Empirical Model, Backup Power Duration, Lead-Acid Battery, Telecommunication System, Mean Absolute Percentage Error (MAPE)Abstract
This study proposes an empirical model for forecasting the backup power duration in telecommunication systems. The model integrates key battery condition indicators-such as service life, efficiency, and the effects of initial discharge—with environmental factors including temperature and load conditions. A 100 Ah lead-acid battery was employed in four case studies: (1) a new battery under controlled temperature, (2) an aged battery under controlled temperature, (3) a new battery under uncontrolled temperature, and (4) an aged battery under uncontrolled temperature. The calculated backup durations obtained from the proposed model were compared with experimental results until the specified terminal voltage was reached. The findings indicate that the proposed empirical model can accurately estimate the backup duration with a Mean Absolute Percentage Error (MAPE) of 11.42%, demonstrating strong agreement with experimental data. The developed model can serve as a practical criterion for assessing the reliability of backup power systems in telecommunication applications and can be effectively applied to energy management and preventive maintenance planning in future work.
References
Future Trends of Information Technology, Prosoft HCM. (Accessed: 2025, September 26). [Online]. Available: https://www.prosofthcm.com/Article/Detail/16795/
C. Chaitip, “Backup power system for telecommunication sites,” B. Eng. Project (Electrical Engineering), Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi, Thailand, 2024.
F. Mahdavian, S. Platt, M. Wiens, M. Klein, and F. Schultmann, “Communication blackouts in power outages: Findings from scenario exercises in Germany and France,” International Journal of Disaster Risk Reduction, vol. 46, Article 101628, Apr. 2020, doi: 10.1016/j.ijdrr.2020.101628.
M. Chen and G. A. Rincón-Mora, “Accurate electrical battery model capable of predicting runtime and I–V performance,” IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 504–511, Jun. 2006.
S. Piller, M. Perrin, and A. Jossen, “Methods for state-of-charge determination and their applications,” Journal of Power Sources, vol. 96, no. 1, pp. 113–120, Jun. 2001.
W. Peukert, “Über die Abhängigkeit der Kapazität von der Entladestromstärke bei Bleiakkumulatoren,” Elektrotechnische Zeitschrift, vol. 20, pp. 287–288, 1897.
I. Buchmann, Batteries in a Portable World: A Handbook on Rechargeable Batteries for Non-Engineers, 3rd ed., Cadex Electronics Inc., 2011.
Y. Zhang, C. Liu, and H. Li, “Reliability analysis of backup power systems for telecommunication networks,” IEEE Transactions on Reliability, vol. 67, no. 3, pp. 1179–1188, Sep. 2018.
R. Sharma, N. Singh, and K. Rao, “Impact of power outage on telecommunication systems: a reliability perspective,” International Journal of Electrical Power & Energy Systems, vol. 132, pp. 107205, May 2021.
A. Gharehghani, J. Gholami, P. Shamsizadeh, and S. Mehranfar, “Effect analysis on performance improvement of battery thermal management in cold weather,” Journal of Energy Storage, vol. 45, pp. 103728, 2022
P. Dawan, K. Sriprapha, S. Kittisontirak, T. Boonraksa, N. Junhuathon et al., “Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model,” Energies, vol. 13, no. 2, p. 351, 2020, doi: 10.3390/en13020351.
N. Khortsriwong, P. Boonraksa, T. Boonraksa, T. Fangsuwannarak, A. Boonsrirat, W. Pinthurat, and B. Marungsri, “Performance of deep learning techniques for forecasting PV power generation: A case study on a 1.5 MWp floating PV power plant,” Energies, vol. 16, no. 5, p. 2119, 2023, doi: 10.3390/en16052119.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Faculty of Industrial Technology, Suan Sunandha Rajabhat University
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของคณะเทคโนโลยีอุตสาหกรรม มหาวิทยาลัยราชภัฎสวนสุนันทา
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับมหาวิทยาลัยราชภัฎสวนสุนันทา และคณาจารย์ท่านอื่นๆในมหาวิทยาลัยฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว
