* Corresponding Author
Hide
Show all
Pengenalan Jenis Beban Listrik menggunakan Fast Fourier Transform dan Neural Network
https://doi.org/10.22146/ijeis.85466 Wahyu Setyo Pambudi(1*), Riza Agung Firmansyah(2), Syahri - Muharom(3)
(1) Electrical Engineering Dept., Institut Teknologi Adhi Tama Surabaya (ITATS)
(2) Electrical Engineering Dept., Institut Teknologi Adhi Tama Surabaya (ITATS)
(3) Electrical Engineering Dept., Institut Teknologi Adhi Tama Surabaya (ITATS)
(*) Corresponding Author
Abstract
The current condition of global energy utilization is that 40% is consumed by residential, this value is higher than industrial and commercial groups. Overcoming this problem can be done through energy conservation management specifically for household customers. The initial process of energy conservation is monitoring the use of electrical energy loads that are being used. Monitoring the type of use of electrical energy loads that have low-cost features is Non-Intrusive Load Monitoring (NILM). The method that can be used to monitor electrical energy loads with NILM is a combination of Fast Fourier Transform (FFT)-Artificial Neural Network (ANN). The success rate of recognizing this type of electrical load depends on the size of the epoch during the ANN training process. Based on testing the success value of being able to achieve a value of 100% if using epoch 10000, it is different if using epoch 500 the success is only up to 30%. The results of the calculation process using the confusion matrix have an accuracy of 0.5876 or 58.76%, while the F1 value is 0.6928 or 69.28%.
Keywords
Full Text:
PDFReferences
IESR, Indonesia Clean Energy Outlook: Tracking Progress and Review of Clean Energy Development in Indonesia. 2019.
J. Zhang, X. Chen, W. W. Y. Ng, C. S. Lai, and L. L. Lai, “New Appliance Detection for Nonintrusive Load Monitoring,” IEEE Trans. Ind. Informatics, vol. 15, no. 8, pp. 4819–4829, 2019, doi: 10.1109/tii.2019.2916213.
A. Riantiarto, D. Suryadi, and Saifurrahman, “Rancang Bangun Alat Monitoring Arus Pada Beban Listrik Rumah Tangga Menggunakan WEB Berbasis Arduino UNO R3,” J. Tek. Elektro Univ. Tanjungpura, vol. 2, no. 1, pp. 1–9, 2019, [Online]. Available: https://jurnal.untan.ac.id/index.php/jteuntan/article/download/35505/75676582854.
I. Nirmalasari, A. E. Putra, and B. N. Prastowo, “Purwarupa Alat Ukur Daya Listrik Berbasis Netduino Plus,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 5, no. 1, p. 21, 2015, doi: 10.22146/ijeis.7150.
M. E. Lutfi and A. Rouf, “Purwarupa kWh Meter Prabayar Berbasis Sensor Network 1,” Ijeis, vol. 4, no. 2, pp. 147–156, 2014.
S. Biansoongnern and B. Plangklang, “Nonintrusive load monitoring (NILM) using an Artificial Neural Network in embedded system with low sampling rate,” 2016 13th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol. ECTI-CON 2016, 2016, doi: 10.1109/ECTICon.2016.7561398.
B. Buddhahai, W. Wongseree, and P. Rakkwamsuk, “An Energy Prediction Approach for a Nonintrusive Load Monitoring in Home Appliances,” IEEE Trans. Consum. Electron., vol. 66, no. 1, pp. 96–105, 2020, doi: 10.1109/TCE.2019.2956638.
T. Le, “Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree,” IEEE Access, vol. 8, pp. 55937–55952, 2020, doi: 10.1109/ACCESS.2020.2981969.
D. S. Kumar, K. L. Low, A. Sharma, and W. L. Woo, “Non-Intrusive Load Monitoring using Feed Forward Neural Network,” 2019 IEEE PES Innov. Smart Grid Technol. Asia, ISGT 2019, pp. 4065–4069, 2019, doi: 10.1109/ISGT-Asia.2019.8880801.
P. Jacko and O. Kravets, “Spectral Analysis by STM32 Microcontroller of the Mixed Signal,” Proc. Int. Conf. Mod. Electr. Energy Syst. MEES 2019, pp. 342–345, 2019, doi: 10.1109/MEES.2019.8896545.
M. Simic, “Nonintrusive identification of residential appliances using harmonic analysis,” Turkish J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 780–791, 2018, doi: 10.3906/elk-1705-262.
DOI: https://doi.org/10.22146/ijeis.85466
Article Metrics
Abstract views : 0
| 
views : 0
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1