Implementasi Metode Hybrid Backpropagation Neural Network dan Particle Swarm Optimization untuk Prediksi Konsumsi Listrik Rumah Tangga Berdasarkan Golongan Tarif : Implementation of Hybrid Backpropagation Neural Network and Particle Swarm Optimization for Predicting Household Electricity Consumption Based On Tariff Categories

Yuni Artha Chyntia Saragih; Erma Suryani

doi:10.57152/malcom.v6i2.2519

Authors

Yuni Artha Chyntia Saragih Institut Teknologi Sepuluh Nopember
Erma Suryani Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.57152/malcom.v6i2.2519

Keywords:

Backpropagation, Konsumsi Listrik, Prediksi, Particle Swarm Optimization, Tarif Rumah Tangga

Abstract

Konsumsi listrik pada sektor rumah tangga terus meningkat seiring dengan pertumbuhan penduduk dan perkembangan sosial ekonomi. Penelitian ini mengusulkan model peramalan hybrid yang mengintegrasikan Backpropagation Neural Network (BPNN) dengan Particle Swarm Optimization (PSO) untuk meningkatkan akurasi prediksi konsumsi listrik rumah tangga berdasarkan golongan tarif. Data historis penjualan listrik bulanan periode 2020 sampai dengan 2024 digunakan sebagai dataset, yang mencakup jumlah pelanggan, daya tersambung, dan konsumsi energi. Hasil penelitian menunjukkan bahwa model BPNN–PSO memiliki kinerja yang lebih baik dibandingkan dengan BPNN murni. Proses optimasi berhasil menurunkan nilai Mean Absolute Percentage Error (MAPE) dari 57,97% menjadi 46,38% serta meningkatkan nilai koefisien determinasi (R²) dari –0,2403 menjadi 0,1831. Model yang diusulkan kemudian digunakan untuk memproyeksikan kebutuhan listrik periode 2025–2029 dan menunjukkan adanya tren pertumbuhan yang konsisten. Temuan ini membuktikan bahwa pendekatan hybrid BPNN–PSO dapat menjadi alat peramalan yang lebih andal dalam mendukung perencanaan dan pengambilan keputusan di sektor ketenagalistrikan.

Downloads

Download data is not yet available.

References

S. Salehimehr, B. Taheri, and M. Sedighizadeh, “Short-term load forecasting in smart grids using artificial intelligence methods: A survey,” The Journal of Engineering, no. 12, pp. 1133–1142, 2022, doi: 10.1049/tje2.12183.

N. Mohammed and A. Al-Bazi, “An adaptive backpropagation algorithm for long-term electricity load forecasting,” Neural Computing & Applications, vol. 34, pp. 477–491, 2021, doi: 10.1007/s00521-021-06384-x.

S. Mateo-Barcos, R. Giralt, A. Sumper, and E. Vinyals, “Forecasting electricity demand of municipalities through artificial neural networks and metered supply point classification,” Energy Reports, vol. 11, pp. 3533–3549, 2024, doi: 10.1016/j.egyr.2024.03.023.

H. D. Purnomo, A. S. Nugroho, and R. M. Pratama, “Neuroevolution untuk optimalisasi parameter jaringan saraf tiruan,” Aiti: Jurnal Teknologi Informasi, vol. 20, no. 2, pp. 125–134, 2023, doi: 10.24246/aiti.v20i2.125-134.

E. Y. Sari, Kusrini, and A. Sunyoto, “Optimization of weight backpropagation with Particle Swarm Optimization for student dropout prediction,” in Proc. 4th Int. Conf. Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, 2019, pp. 423–428, doi: 10.1109/ICITISEE48480.2019.9004032.

E. Ofori-Ntow Jnr and Y. Y. Ziggah, “Electricity demand forecasting based on feature extraction and optimized backpropagation neural network,” e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 6, p. 100293, 2023, doi: 10.1016/j.prime.2023.100293.

A. Agustyawan, T. G. Laksana, and U. Athiyah, “Combination of BPNN and PSO for water production prediction,” Scientific Journal of Informatics, vol. 9, no. 1, pp. 84–94, 2022, doi: 10.15294/sji.v9i1.29849.

F. E. Bezerra et al., “Forecasting and minimizing electricity costs in Brazil,” Engineering Review, vol. 41, no. 3, pp. 65–75, 2021, doi: 10.30765/ER.1529.

M. W. Hasan, “Energy consumption forecasting using LSTM,” Measurement: Energy, vol. 5, p. 100033, 2025, doi: 10.1016/j.meaene.2024.100033.

A. Hussein and M. Awad, “Hybrid RNN and GA for electricity forecasting,” Measurement: Energy, vol. 2, p. 100004, 2024, doi: 10.1016/j.meaene.2024.100004.

Y.-S. Kim et al., “Impact of normalization on electricity prediction,” Sustainable Cities and Society, vol. 118, p. 105570, 2024, doi: 10.1016/j.scs.2024.105570.

M. Joshi and A. Arora, “BPNN-PSO for power quality improvement,” in Proc. INCET, 2022, doi: 10.1109/INCET54531.2022.9824451.

S. Sena et al., “Determinants of electricity consumption for households,” Sustainability, vol. 13, no. 2, p. 818, 2021, doi: 10.3390/su13020818.

J. P. Singh, O. Alam, and A. Yassine, “Geodemographic factors in electricity forecasting,” IEEE Access, vol. 10, pp. 70456–70466, 2022, doi: 10.1109/ACCESS.2022.3188004.

N. Selle, N. Yudistira, and C. Dewi, “Comparison of LSTM and RNN for electricity prediction,” Jurnal TI, vol. 9, no. 1, pp. 155–162, 2022, doi: 10.25126/jtiik.2022915585.

Widiartha et al., “Hybrid BPNN-PSO for accuracy improvement,” Current Journal of Applied Science and Technology, vol. 42, no. 6, pp. 10–18, 2023, doi: 10.9734/cjast/2023/v42i64072.

G. Quan et al., “PSO-BP integrated model for material behavior,” Materials, vol. 16, no. 8, 2023, doi: 10.3390/ma16082982.

Tyastama et al., “Hybrid BPNN-PSO for disease prediction,” JINITA, vol. 3, no. 1, pp. 9–16, 2021, doi: 10.35970/jinita.v3i1.588.

R. Dharmawan, A. Hasniaty, and Y. S. Akil, “Household electricity consumption analysis,” Jurnal Penelitian Enjiniring, vol. 26, no. 1, pp. 34–40, 2023, doi: 10.25042/jpe.052022.05.

D. H. Vo, “Socio-demographic determinants of electricity consumption,” PLOS ONE, vol. 20, no. 4, 2025, doi: 10.1371/journal.pone.0320758.

H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, “A review of deep learning for renewable energy forecasting,” Energy Conversion and Management, vol. 198, p. 111799, 2021. Available at: https://doi.org/10.1016/j.enconman.2019.111799.

J. Kong, H. Dong, J. Jia, D. Hill, Y. Xu, and Y. Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network,” IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 841–851, 2020. Available at: https://doi.org/10.1109/TSG.2017.2753802.

Y. Yu, Y. Chen, Y. Yong, and Y. Li, “A hybrid model for electricity consumption forecasting using deep learning and optimization algorithms,” Applied Energy, vol. 285, p. 116405, 2021. Available at: https://doi.org/10.1016/j.apenergy.2021.116405.

S. Mirjalili, “Particle Swarm Optimization: theory, literature review, and application in energy systems,” IEEE Access, vol. 7, pp. 306–317, 2019. Available at: https://doi.org/10.1109/ACCESS.2018.2880742.

X. Qiu, L. Zhang, Y. Ren, P. N. Suganthan, and G. Amaratunga, “Ensemble deep learning for electricity load forecasting,” Applied Soft Computing, vol. 92, p. 106304, 2020. Available at: https://doi.org/10.1016/j.asoc.2020.106304.