Analisis Pengaruh Noise pada Performa K-Nearest Neighbors Algorithm dengan Variasi Jarak untuk klasifikasi Beban Listrik

ARIS SURYA YUNATA, ABDUL HALIM, HILDA LUTHFIYAH

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ABSTRAK

Teknik Non-Intrusive Load Monitoring (NILM) digunakan dalam pemantauan konsumsi energi. Variabel pengukuran yang digunakan yaitu Real Power dan Reactive Power. klasifikasi beban listrik menjadi acuan dalam mengurangi tagihan energi. Namun, data pengukuran sering kali terpengaruh oleh noise. Penelitian ini bertujuan untuk menganalisis pengaruh noise terhadap performa algoritma k-Nearest Neighbors (k-NN) dalam klasifikasi beban listrik. Berbagai tingkat noise secara rundom diberikan pada data pengukuran yang diperoleh. Selanjutnya, model k-NN dilatih dan dievaluasi dengan nilai k = 1 sampai 9 dan 15 tipe jarak. Hasil eksperimen menunjukkan bahwa penambahan noise pada data pengukuran secara signifikan mempengaruhi performa algoritma k-NN dalam mengklasifikasikan beban listrik. Pengaruh ini terlihat pada nilai akurasi tertinggi mayoritas pada k = 3 dan Tipe jarak Cambera menghasilkan nilai akurasi di atas rata-rata.

Kata kunci: NILM, Real Power, Reactive Power, noise, k-NN

 

ABSTRACT

The Non-Intrusive Load Monitoring (NILM) technique is used in monitoring energy consumption. The measurement variables used are Real Power and Reactive Power. Electric load classification serves as a reference in reducing energy bills. However, measurement data is often affected by noise. This study aims to analyze the influence of noise on the performance of the k-Nearest Neighbors (k-NN) algorithm in electric load classification. Various levels of noise are randomly added to the obtained measurement data. Subsequently, the k-NN model is trained and evaluated with values of k = 1 to 9 and 15 distance types. The experimental results show that the addition of noise to the measurement data significantly affects the performance of the k-NN algorithm in classifying electric loads. This influence is observed in the highest accuracy values, mostly at k = 3, and the Canberra distance type yields accuracy values above average.

Keywords: NILM, Real Power, Reactive Power, noise, k-NN


Kata Kunci


NILM; Real Power; Reactive Power; noise; k-NN

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Referensi


Bucci, G., Ciancetta, F., Fiorucci, E., Mari, S., & Fioravanti, A. (2021). Measurements for nonintrusive load monitoring through machine learning approaches. Acta IMEKO, 10(4) 90–96. https://doi.org/10.21014/acta_imeko.v10i4.1184

C, S., Kumar. M., & Karuppasamy, I. (2019). Design and Implementation of Non-Intrusive Load Monitoring using Machine Learning Algorithm for Appliance Monitoring. 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), (pp. 1–6). https://doi.org/10.1109/INCOS45849.2019.8951312

Fawzi, A., Moosavi-Dezfooli, S., & Frossard, P. (2016). Robustness of classifiers: from adversarial to random noise. Conference on Neural Information Processing Systems (NIPS).

Hidiyanto, F., & Halim, A. (2020). K-NN Methods with Varied k, Distance and Training Data to Disaggregate NILM with Similar Load Characteristic. Asia Pacific Conference on Research in Industrial and Systems Engineering (APCORISE), (pp. 93–99). https://doi.org/10.1145/3400934.3400953

Iqbal, H. K., Malik, F. H., Muhammad, A., Qureshi, M. A., Abbasi, M. N., & Chishti, A. R. (2021). A critical review of state-of-the-art non-intrusive load monitoring datasets. Electric Power Systems Research, 192, 106921. https://doi.org/10.1016/j.epsr.2020.106921

Khan, M. M. R., Siddique, Md. A. B., & Sakib, S. (2019). Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors. 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), (pp. 1–5). https://doi.org/10.1109/ICIET48527.2019.9290671

Li, Y., Wang, H., Yang, J., Wang, K., & Qi, G. (2021). A non-intrusive load monitoring algorithm based on multiple features and decision fusion. Energy Reports, 7, 1555–1562. https://doi.org/10.1016/j.egyr.2021.09.087

Makonin, S., Ellert, B., Bajic, I. V., & Popowich, F. (2016). Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Scientific Data, 3. https://doi.org/10.1038/sdata.2016.37

Makonin, S., Popowich, F., Bartram, L., Gill, B., & Bajic, I. V. (2013). AMPds: A public dataset for load disaggregation and eco-feedback research. 2013 IEEE Electrical Power & Energy Conference, (pp. 1–6). https://doi.org/10.1109/EPEC.2013.6802949

Makonin, Stephen. (2016). AMPds2: The Almanac of Minutely Power dataset (Version2). Retrieved from dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN

/FIE0S4.

McNeil, M. A., Karali, N., & Letschert, V. (2019). Forecasting Indonesia's electricity load through 2030 and peak demand reductions from appliance and lighting efficiency. Energy for Sustainable Development, 49, 65–77. https://doi.org/10.1016/j.esd.2019.01.001

Mohajan, H. K. (2017). Two Criteria for Good Measurements in Research: Validity and Reliability Two Criteria for Good Measurements in Research: Validity and Reliability. Munich Personal RePEc Archive, 17(3), 58–82.

Prasath, V. B. S., Abu Alfeilat, H. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Eyal Salman, H. S. (2019). Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review. Big Data, 7(4), 221–248. https://doi.org/10.1089/big.2018.0175

Primartha, R. (2018). Belajar Machine Learning Teori dan Praktik. Penerbit Informatika.

Ruano, A., Hernandez, A., Urena, J., Ruano, M., & Garcia, J. (2019). NILM techniques for intelligent home energy management and ambient assisted living: A review. Energies 12(11). MDPI AG. https://doi.org/10.3390/en12112203

Santoso, H. B., Prajogo, S., & Mursid, S. P. (2018). Pengembangan Sistem Pemantauan Konsumsi Energi Rumah Tangga Berbasis Internet of Things (IoT). ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 6(3), 357.h ttps://doi.org/10.26760/elkomika.v6i3.357

Shabbir, N., Vassiljeva, K., Nourollahi Hokmabad, H., Husev, O., Petlenkov, E., & Belikov, J. (2024). Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring. Electronics, 13(8), 1420. https://doi.org/10.3390/electronics13081420

Surahman, U., Hartono, D., Setyowati, E., & Jurizat, A. (2022). Investigation on household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic. Energy and Buildings, 261. https://doi.org/10.1016/j.enbuild.2022.111956

Wahyono, W., Trisna, I. N. P., Sariwening, S. L., Fajar, M., & Wijayanto, D. (2020). Comparison of distance measurement on k-nearest neighbour in textual data classification. Jurnal Teknologi dan Sistem Komputer, 8(1), 54–58. https://doi.org/10.14710/jtsiskom.8.1.2020.54-58

Yang, C. C., Soh, C. S., & Yap, V. V. (2018). A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency. Energy Efficiency, 11(1), 239–259. https://doi.org/10.1007/s12053-017-9561-0




DOI: https://doi.org/10.26760/elkomika.v12i3.745

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