Sistem Kontrol Time Based Scheduling Menggunakan PLC Untuk Efisiensi Konsumsi Daya Listrik Pada Studio Musik.
Keywords:
Time Based Scheduling, PLC, HMI, Faktor Daya, Efisiensi DayaAbstract
Unscheduled electricity usage in music studios may lead to energy wastage due to electrical loads remaining switched on outside operating hours. This study aims to design and implement a Time-Based Scheduling system using an Omron Programmable Logic Controller (PLC) and a Haiwell Human–Machine Interface (HMI) to automate the operation of electrical loads according to predetermined schedules. The system is equipped with a PZEM sensor to monitor electrical parameters in real time and a capacitor to compensate reactive power and improve the power factor. the research method consisted of hardware and software design, system implementation, and performance testing. System performance was evaluated by comparing electricity consumption before and after the implementation of the Time-Based Scheduling system. In addition, the accuracy of the PZEM sensor was assessed by comparing its measurements with those obtained using a clamp meter. the results demonstrate that the system successfully controlled electrical loads automatically according to the predefined schedule. The PZEM sensor produced measurements that closely matched the reference instrument, with average deviations of 2.24 V for voltage and 0.0058 A for current. Electricity consumption decreased from 55 Wh to 52 Wh, resulting in an energy saving of 5.45%. Although the reduction in energy consumption was relatively modest, the proposed system improved operational discipline, reduced the potential for human error, and supported more efficient electricity management.
Downloads
References
[1] C. de Bakker, M. B. C. Aries, H. S. M. Kort, and A. L. P. Rosemann, “Occupancy-based lighting control in open-plan office spaces: A state-of-the-art review,” Building and Environment, vol. 112, pp. 308–321, 2017, doi: 10.1016/j.buildenv.2016.11.042.
[2] C. de Bakker, T. van de Voort, and A. L. P. Rosemann, “The energy saving potential of occupancy-based lighting control strategies in open-plan offices: The influence of occupancy patterns,” Energies, vol. 11, no. 1, 2018, doi: 10.3390/en11010002.
[3] S. J. Kang, J. Park, K. Y. Oh, J. G. Noh, and H. Park, “Scheduling-based real time energy flow control strategy for building energy management system,” Energy and Buildings, vol. 75, pp. 239–248, 2014, doi: 10.1016/j.enbuild.2014.02.008.
[4] C. Chen, J. Wang, Y. Heo, and S. Kishore, “MPC-based appliance scheduling for residential building energy management controller,” IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1401–1410, 2013, doi: 10.1109/TSG.2013.2265239.
[5] A. R. Kiran, B. Venkat Sundeep, Ch. Sree Vardhan, and L. Mathews, “The principle of programmable logic controller and its role in automation,” International Journal of Engineering Trends and Technology, vol. 4, no. 3, pp. 500–502, 2013, doi: 10.14445/22315381/IJETT-V4I3P250.
[6] D. Yuhendri, “Penggunaan PLC sebagai pengontrol peralatan building automatis,” JET (Journal of Electrical Technology), vol. 3, no. 3, 2018, doi: 10.30743/jet.v3i3.952.
[7] A. Al Ka’bi, “Energy consumption management using programmable logic controllers (PLC’s),” in 2021 IEEE Technology & Engineering Management Conference-Europe (TEMSCON-EUR), 2021, doi: 10.1109/TEMSCON-EUR52034.2021.9488607.
[8] Waluyo, A. Widura, and W. A. Purbandoko, “Energy-saving in air conditioners using PLC control and the SCADA monitoring system,” ECTI Transactions on Electrical Engineering, Electronics, and Communications, 2022, doi: 10.37936/ecti-eec.2022201.246095.
[9] Syafrudi and D. W. A. Ningtias, “Simulasi sistem otomasi rumah hemat energi berbasis programmable logic controller,” Jurnal Teknik Elektro dan Komputasi (ELKOM), 2023.
[10] P. Anggraeni et al., “An industrial IoT architecture for energy-efficient lighting and HVAC control using PLC (CtrlX CORE) and presence sensing,” 2025.
[11] D. Saputra, A. I. Juliswanto, and Zulfachmi, “Sistem perhitungan waktu rental studio musik berbasis Arduino menggunakan validasi RFID,” Jurnal Bangkit Indonesia, vol. 9, no. 1, pp. 57–61, 2020.
[12] F. Maulana, A. Sunawar, and N. H. Yuninda, “Sistem pemantauan dan kontrol waktu pada studio musik menggunakan RFID berbasis Internet of Things (IoT),” Journal of Electrical Vocational Education and Technology, vol. 7, no. 1, 2024, doi: 10.21009/JEVET.0071.04.
[13] A. Shinde, T. Pisal, and G. Kumbhar, “Energy management using PLC,” SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, vol. 17, no. 2, pp. 8–10, 2025, doi: 10.18090/samriddhi.v17i02.03.
[14] O. M. Aloquili and N. M. Abu-Shikhah, “Power factor correction capacitors for utilising power consumption in industrial plants,” International Journal of Energy Technology and Policy, vol. 7, no. 3, pp. 288–308, 2010, doi: 10.1504/IJETP.2010.033100.
[15] S. A. Sadat, E. Sreesobha, and P. V. N. Prasad, “Power factor correction of inductive loads using PLC,” 2018.
[16] O. O. Olusanya, G. M. Adebajo, I. Giwa, K. Okokpujie, S. A. Daramola, and A. V. Akingunsoye, “Neuro-fuzzy logic controller for switching capacitor banks in power factor correction within the manufacturing industry,” Journal of Intelligent Systems and Control, vol. 3, no. 2, pp. 93–106, 2024, doi: 10.56578/jisc030203.
[16] D. Nallaperuma et al., “Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4679–4690, 2019, doi: 10.1109/TITS.2019.2924883.
[17] S. Schulz, M. Becker, M. R. Groseclose, S. Schadt, and C. Hopf, “Advanced MALDI mass spectrometry imaging in pharmaceutical research and drug development,” Curr. Opin. Biotechnol., vol. 55, pp. 51–59, 2019, doi: 10.1016/j.copbio.2018.08.003.
[18] C. Shang and F. You, “Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era,” Engineering, vol. 5, no. 6, pp. 1010–1016, 2019, doi: 10.1016/j.eng.2019.01.019.
[19] Y. Yu, M. Li, L. Liu, Y. Li, and J. Wang, “Clinical big data and deep learning: Applications, challenges, and future outlooks,” Big Data Min. Anal., vol. 2, no. 4, pp. 288–305, 2019, doi: 10.26599/BDMA.2019.9020007.
[20] M. Huang, W. Liu, T. Wang, H. Song, X. Li, and A. Liu, “A queuing delay utilization scheme for on-path service aggregation in services-oriented computing networks,” IEEE Access, vol. 7, pp. 23816–23833, 2019, doi: 10.1109/ACCESS.2019.2899402.
[21] G. Xu, Y. Shi, X. Sun, and W. Shen, “Internet of things in marine environment monitoring: A review,” Sensors (Switzerland), vol. 19, no. 7, pp. 1–21, 2019, doi: 10.3390/s19071711.
[22] M. Aqib, R. Mehmood, A. Alzahrani, I. Katib, AL beshri, and S. M. Altuwaijri, Smarter traffic prediction using big data, in-memory computing, deep learning and gpus, vol. 19, no. 9. 2019.
[23] S. Leonelli and N. Tempini, Data Journeys in the Sciences. 2020.
[24] N. Stylos and J. Zwiegelaar, Big Data as a Game Changer: How Does It Shape Business Intelligence Within a Tourism and Hospitality Industry Context? 2019.
[25] Q. Song, H. Ge, J. Caverlee, and X. Hu, “Tensor completion algorithms in big data analytics,” arXiv, vol. 13, no. 1, 2017.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gita Rolland Ibanez, Ayusta Lukita Wardani, Daeng Rahmatullah, As’ad Shidqy Aziz (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
