Prediksi Beban Listrik Menggunakan Algoritma Jaringan Syaraf Tiruan Tipe Propagasi-Balik

Ramadoni Syahputra, Febrian Dhimas Syahfitra, Karisma Trinanda Putra, Indah Soesanti

Abstract


Artikel ini mengusulkan prediksi beban puncak menggunakan metode jaringan syaraf tiruan tipe propagasi-balik. Prediksi beban puncak transformator tenaga merupakan tugas penting dalam mengantisipasi pertumbuhan beban listrik di masa mendatang. Prediksi yang tepat dan akurat akan memfasilitasi perencanaan kapasitas pembangkit listrik yang memadai pada waktu yang tepat. Metode jaringan syaraf tiruan tipe propagasi-balik memiliki akurasi yang baik dalam tugas-tugas prediksi. Pada penelitian ini dilakukan prediksi beban puncak pada dua buah transformator tenaga dengan studi kasus di Gardu Induk Bumiayu, Brebes, Jawa Tengah, Indonesia. Parameter pelatihan adalah data pertumbuhan penduduk, produk domestik regional bruto (PDRB), dan data beban puncak selama sepuluh tahun terakhir. Hasil penelitian menunjukkan bahwa kedua unit transformator tenaga tersebut masih dapat melayani beban listrik di wilayah pelayanan Gardu Induk Bumiayu selama sepuluh tahun ke depan.  

 

This article proposes a peak load prediction using the backpropagation neural network method. Predicting the peak load of power transformers is an important task in anticipating load growth in the future. Precise and accurate predictions will facilitate the planning of sufficient power generation capacity at the right time. The backpropagation type neural network method has good accuracy in the prediction task. In this study, a case study was carried out by predicting the peak load of power transformers at Bumiayu Substation, Brebes, Central Java, Indonesia. Training parameters consists of population growth data, gross regional domestic product (GRDP), and peak load data for the last ten years. The results showed that the two power transformer units could still serve the electricity load in the Bumiayu substation service area for the next ten years.   


Keywords


Peak load prediction, power transformer, substation, artificial neural network, backpropagation

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References


Afrasiabi, M., Mohammadi, M., Rastegar, M., Stankovic, L.,

Afrasiabi, S. (2020). Deep-Based Conditional Probability Density Function Forecasting of Residential Loads. IEEE Transactions on Smart Grid, Vol. 11, Issue 4, July 2020, pp. 3646 – 3657.

Alfieri, L., Falco, P.D. (2020). Wavelet-Based Decompositions in Probabilistic Load Forecasting. IEEE Transactions on Smart Grid, Vol. 11, Issue 2, March 2020, pp. 1367 – 1376.

Anonim. (2019). Statistik PLN 2018. Sekretaris Perusahaan PT PLN (Persero), Jakarta.

Cao, Z., Wan, C., Zhang, Z., Li, F., Song, Y. (2020). Hybrid Ensemble Deep Learning for Deterministic and Probabilistic Low-Voltage Load Forecasting. IEEE Transactions on Power Systems, Vol. 35, Issue 3, May 2020, pp. 1881 – 1897.

Chen, K., Chen, K., Wang, Q., He, Z., Hu, J., He, J. (2018). Short-Term Load Forecasting With Deep Residual Networks. IEEE Transactions on Smart Grid, Vol. 10, Issue 4, July 2019, pp. 3943 – 3952.

Chen, Q., Xia, M., Lu, T., Jiang, X., Liu, W., Sun, Q. (2019). Short-Term Load Forecasting Based on Deep Learning for End-User Transformer Subject to Volatile Electric Heating Loads. IEEE Access, Vol. 7, 2019, pp. 162697 - 162707.

Deng, Z., Wang, B., Xu, Y., Xu, T., Liu, C., Zhu, Z. (2019). Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting. IEEE Access, Vol. 7, 2019, pp. 88058 – 88071.

Feng, C., Sun, M., Zhang, J. (2019). Reinforced Deterministic and Probabilistic Load Forecasting via Q -Learning Dynamic Model Selection. IEEE Transactions on Smart Grid, Vol. 11, Issue 2, March 2020, pp. 1377 – 1386.

Haq, M.R., Ni, Z. (2019). A New Hybrid Model for Short-Term Electricity Load Forecasting. IEEE Access, Vol. 7, 2019, pp. 125413 - 125423.

Hong, Y., Zhou, Y., Li, Q., Xu, W., Zheng, X. (2020). A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid. IEEE Access, Vol. 8, 2020, pp. 55785 - 55797.

Huang, N., Wang, W., Wang, S., Wang, J., Cai, G., Zhang, L. (2020). Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting. IEEE Access, Vol. 8, 2020, pp. 25198 – 25209.

Jonan, I. (2019). Rencana Umum Ketenagalistrikan Nasional 2019-2038, Kementerian Energi dan Sumber Daya Mineral, Jakarta.

Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y. (2017). Short-Term Residential Load Forecasting Based on Resident Behaviour Learning. IEEE Transactions on Power Systems, Vol. 33, Issue 1, Jan. 2018, pp. 1087 – 1088.

Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y. (2019). Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid, Vol. 10, Issue 1, Jan. 2019, pp. 841 – 851.

Li, B., Zhang, J., He, Y., Wang, Y. (2017). Short-Term Load-Forecasting Method Based on Wavelet Decomposition With Second-Order Gray Neural Network Model Combined With ADF Test. IEEE Access, Vol. 5, 2017, pp. 16324 - 16331.

Li, T., Wang, Y., Zhang, N. (2019). Combining Probability Density Forecasts for Power Electrical Loads. IEEE Transactions on Smart Grid, Vol. 11, Issue 2, March 2020, pp. 1679 – 1690.

Ouyang, T., He, Y., Li, H., Sun, Z., Baek, S. (2019). Modeling and Forecasting Short-Term Power Load With Copula Model and Deep Belief Network. IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 3, Issue 2, April 2019, pp. 127 – 136.

Park, K., Yoon, S., Hwang, E. (2019). Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals. IEEE Access, Vol. 7, 2019, pp. 12297 - 12306.

Siang, J.J. (2009). Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan MATLAB. Penerbit ANDI, Yogyakarta.

Syahputra, R., Soesanti, I. (2017a). Modeling of Wind Power Plant with Doubly-Fed Induction Generator. Jurnal Teknologi, Journal of Electrical Technology UMY (JET-UMY), 1(3), pp. 126-134.

Syahputra, R. (2017b). Distribution Network Optimization Based on Genetic Algorithm. Jurnal Teknologi, Journal of Electrical Technology UMY (JET-UMY), 1(1), pp. 1-9.

Tamizharasi, G. (2014). Energy Forecasting using Artificial Neural Networks. IJAREEIE, Vol. 3, Issue 3. March 2014, pp. 7568-7576.

Wang, Y., Chen, Q., Zhang, N., Wang, Y. (2018a). Conditional Residual Modeling for Probabilistic Load Forecasting. IEEE Transactions on Power Systems, Vol. 33, Issue 6, Nov. 2018, pp. 7327 – 7330.

Wang, Y., Chen, Q., Sun, M., Kang, C., Xia, Q. (2018b). An Ensemble Forecasting Method for the Aggregated Load With Subprofiles. IEEE Transactions on Smart Grid, Vol. 9, Issue 4, July 2018, pp. 3906 – 3908.s




DOI: https://doi.org/10.18196/st.v23i2.9940

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