PREDICTION OF THE ELECTRIC POWER BY OSCILLATING WATER COLUMN WAVE POWER PLANTS ON BAWEAN ISLAND USING LSTM
Abstract
The demand for electricity in Indonesia continues to increase in line with population growth and the expansion of economic development. This increase is not matched by the diminishing electricity resources, as fossil fuels, which are non-renewable, are being used. Therefore, there is a need for renewable energy sources that can be utilized as long-term electricity resources. The abundant marine areas in Indonesia make it a potential source of alternative energy, one form of its utilization is the Ocean Wave Power Plant using the Oscillating Water Column (OWC) method. Bawean Island in Gresik is one of the regions that has this potential, while also facing long-standing electricity supply limitations that have resulted in uneven electricity distribution among the community. The problem does not stop at power generation but also extends to the transmission system between supply and demand. This research is conducted to predict the electricity generated by the ocean wave power plant to help avoid mismatches when supplying electricity. This study uses time series data from January 1st, 2021, to May 5th, 2024, which includes wave height, length, period, and amplitude. Electricity prediction based on these parameters can be performed using deep learning-based methods that can effectively process sequential time series data, such as the Long Short Term Memory (LSTM) method, by experimenting with the number of neurons, epochs, and batch sizes. The best prediction results for the variables of height, length, period, and amplitude of the waves obtained MAPE values of 0.3657%, 0.1637%, 0.0888%, and 0.3480%, respectively. The electricity prediction results from the best parameters obtained a MAPE of 0.3549%.
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J. U. Jasron, D. P. Mangesa, K. Boimau, B. V. Tarigan, E. U. K. Maliwemu, and M. Salombe, “ANALISA POTENSI GELOMBANG LAUT SEBAGAI SUMBER ENERGI TERBARUKAN MENGGUNAKAN PERANGKAT OSCILLATING WATER COLUMN (OWC) DI WILAYAH PERAIRAN LAUT TIMOR,” LONTAR J. Tek. Mesin Undana, vol. 9, no. 01, pp. 14–20, 2022, doi: https://doi.org/10.35508/ljtmu.v9i01.7269.
S. Nengsih, “POTENSI AIR LAUT ACEH SEBAGAI SUMBER ENERGI LISTRIK ALTERNATIF,” CIRCUIT J. Ilm. Pendidik. Tek. Elektro, vol. 4, no. 2, p. 81, 2020, doi: https://doi.org/10.22373/crc.v4i2.6496.
A. Vidura, R. L. W, and Mukhtasor, “POTENSI PEMANFAATAN PEMBANGKIT LISTRIK TENAGA GELOMBANG LAUT DI PERAIRAN SELATAN PULAU JAWA DALAM MENDUKUNG KETAHANAN ENERGI,” J. Ketahanan Energi, vol. 8, no. 1, pp. 32–48, 2022.
A. Rohman and H. Yuliandoko, “STUDI KARAKTERISTIK PEMBANGKIT LISTRIK TENAGA GELOMBANG AIR LAUT (PLTGL) SEBAGAI ENERGI TERBARUKAN,” Semin. Nas. Terap. Ris. Inov. Ke-6, vol. 6, no. 1, pp. 129–137, 2020.
B. Y. Suprapto, “DESAIN PENGEMBANGAN SISTEM PEMBANGKIT LISTRIK TENAGA GELOMBANG LAUT BERBASIS KESEIMBANGAN GYROSCOPE,” J. Surya Energy, vol. 5, no. 2, pp. 50–54, 2022, doi: https://doi.org/10.32502/jse.v5i2.3328.
S. Setiyawan and N. Abdulrahim, “PEMBANGKIT LISTRIK TENAGA GELOMBANG LAUT DENGAN MENGGUNAKAN TEKNOLOGI OSCILATING WATER COLUMN (OWC) DI PERAIRAN MARANA,” REKONSTRUKSI TADULAKO Civ. Eng. J. Res. Dev., pp. 59–68, 2021, doi: https://doi.org/10.22487/renstra.v2i1.224.
D. K. Sari and S. Fahrezy, “Pengaruh Angin Terhadap Karakteristik Gelombang Laut Di Pulau SAUGI Kabupaten Pangkajene dan Kepulauan,” Indones. J. Geogr., vol. 1, no. 1, pp. 11–19, 2023, [Online]. Available: https://doi.org/10.26858/ijag
N. H. Sarira and P. R. Pong-Masak, “SEAWEED SELECTION TO SUPPLY SUPERIOR SEEDS FOR CULTIVATION,” J. Perikan. Univ. Gadjah Mada, vol. 20, no. 2, p. 79, 2019, doi: https://doi.org/10.22146/jfs.36109.
T. R. Yudisthira, “ANALISIS DAN IMPLEMENTASI ALGORITMA PEMANTAUAN KUALITAS ENERGI LISTRIK PADA SISTEM SMART GRID,” Cosm. J. Tek., vol. 1, no. 4, pp. 165–174, 2024.
B. Y. Phiadelvira, D. Z. Haq, D. C. R. Novitasari, and F. Setiawan, “PREDIKSI BESAR DAYA LISTRIK TENAGA GELOMBANG LAUT METODE OSCILLATING WATER COLOUMN (PLTGL-OWC) DI BANYUWANGI MENGGUNAKAN EXTREME LEARNING MACHINE (ELM),” Unnes J. Math., vol. 11, no. 1, pp. 1–7, 2022, doi: https://doi.org/10.15294/ujm.v11i1.50967.
C. Gu and H. Li, “REVIEW ON DEEP LEARNING RESEARCH AND APPLICATIONS IN WIND AND WAVE ENERGY,” Energies, vol. 15, no. 4, 2022, doi: https://doi.org/10.3390/en15041510.
S. Poornima and M. Pushpalatha, “PREDICTION OF RAINFALL USING INTENSIFIED LSTM BASED RECURRENT NEURAL NETWORK WITH WEIGHTED LINEAR UNITS,” Atmosphere (Basel)., vol. 10, no. 11, 2019, doi: https://doi.org/10.3390/atmos10110668.
M. Musfiroh, D. C. R. Novitasari, P. K. Intan, and G. G. Wisnawa, “PENERAPAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) DAN LONG SHORT TERM MEMORY (LSTM) DALAM MEMPREDIKSI PREDIKSI CURAH HUJAN HARIAN,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 1–11, 2023, doi: https://doi.org/10.47065/bits.v5i1.3114.
M. Rajendar, D. M. Reddy, M. Nagesh, and V. Nagaraju, “PROGRESSION OF COVID-19 CASES IN TELANGANA STATE BY USING ARIMA, MLP, ELM AND LSTM PREDICTION MODELS BY RETROSPECTIVE CONFIRMATION,” Indian J. Sci. Technol., vol. 17, no. 12, pp. 1159–1166, 2024, doi: https://doi.org/10.17485/IJST/v17i12.211.
M. Zulhaidir and M. Arman, “ANALISIS KEAMANAN DAN STABILITAS BANGUNAN PESISIR TERHADAP HANTAMAN GELOMBANG DI PANTAI MERPATI, KAB. BULUKUMBA,” Arus J. Sains dan Teknol. ( AJST ), vol. 2, no. 1, 2024.doi: https://doi.org/10.57250/ajst.v2i1.336
P. R. F. Teixeira and E. Didier, “NUMERICAL ANALYSIS OF THE RESPONSE OF AN ONSHORE OSCILLATING WATER COLUMN WAVE ENERGY CONVERTER TO RANDOM WAVES,” Energy, vol. 220, p. 119719, 2021, doi: https://doi.org/10.1016/j.energy.2020.119719.
S. Arrohman and D. A. Himawanto, “PELUANG PELUANG DAN TANTANGAN PENGEMBANGAN TEKNOLOGI OSCILATING WATER COLUMN (OWS) DI INDONESIA.,” J. Energi dan Teknol. Manufaktur, vol. 4, no. 01, pp. 37–42, 2021, doi: https://doi.org/10.33795/jetm.v4i01.24.
F. Y. Nagifea, “POTENSI PEMBANGKIT LISTRIK TENAGA GELOMBANG LAUT (PLTGL) SEBAGAI ENERGI ALTERNATIF DI INDONESIA,” J. Technopreneur, vol. 10, no. 2, pp. 17–24, 2022, doi: https://doi.org/10.30869/jtech.v10i2.968.
D. Mahroni. Supriyatna, “ENERGI BARU TERBARUKAN DALAM PEMBANGUNAN YANG BERKELANJUTAN DAN PEMANFAATAN ENERGI TERBARUKAN,” Kohesi J. Multidisiplin Saintek, vol. 2, no. 11, pp. 66–76, 2024.
C. A. Siregar and S. Lubis, “PERENCANAAN INSTRUMEN KONVERSI ENERGI TENAGA GELOMBANG DENGAN MENGGUNAKAN TEKNIK KOLOM OSILASI,” J. MESIL (Mesin Elektro Sipil), vol. 1, no. 1, pp. 63–71, 2020, doi: https://doi.org/10.53695/jm.v1i1.156.
S. Rohmaniatul, A. F. Pratiwi, and S. Rahmat, “RANCANG BANGUN PEMBANGKIT LISTRIK TENAGA GELOMBANG LAUT MENGGUNAKAN SISTEM OSCILLATING WATER COLUMN,” Infotekmesin, vol. 12, no. 1, pp. 42–49, 2021, doi: https://doi.org/10.35970/infotekmesin.v12i1.412.
L. N. Nur Afifah and I. T. Safira, “OPTIMALISASI DESAIN TURBIN WELLS PADA SISTEM OSILASI KOLOM AIR PEMBANGKIT LISTRIK TENAGA GELOMBANG LAUT SEBAGAI UPAYA MENINGKATKAN POTENSI SUPPLY ENERGI TERBARUKAN PADA MASYARAKAT PESISIR,” J. Offshore Oil, Prod. Facil. Renew. Energy, vol. 4, no. 2, pp. 26–37, 2020, doi: https://doi.org/10.30588/jo.v4i2.831.
M. T. A. Firdaus, M. Syaukani, A. S. Irfan, A. P. M. Jaya, and R. Yanuar, “STUDI KELAYAKAN POTENSI PEMBANGKIT LISTRIK GELOMBANG LAUT OSCILLATING WATER COLUMN (PLTGL-OWC) DI PERAIRAN PESISIR BARAT LAMPUNG,” Proceeding Technol. Renew. Energy Dev. Conf. 2, vol. 3, no. 1, pp. 21–30, 2023.
I. Gede et al., “IMPLEMENTASI METODE ANALYTICAL HIERARCHY PROCESS DAN INTERPOLASI LINIER DALAM PENENTUAN LOKASI WISATA DI KABUPATEN KARANGASEM,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 5, no. 2, pp. 866–878, 2021, [Online]. Available: https://www.tunasbangsa.ac.id/ejurnal/index.php/jsakti/article/view/383
M. N. Fadilah, A. Yusuf, and N. Huda, “PREDIKSI BEBAN LISTRIK DI KOTA BANJARBARU MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION,” J. Mat. Murni Dan Terap. Epsil., vol. 14, no. 2, p. 81, 2021, doi: https://doi.org/10.20527/epsilon.v14i2.2961.
S. Zahara and S. Sugianto, “PREDIKSI INDEKS HARGA KONSUMEN KOMODITAS MAKANAN BERBASIS CLOUD COMPUTING MENGGUNAKAN MULTILAYER PERCEPTRON,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 6, no. 1, p. 21, 2021, doi: https://doi.org/10.31328/jointecs.v6i1.1702.
F. T. Admojo and Y. I. Sulistya, “ANALISIS PERFORMA ALGORITMA STOCHASTIC GRADIENT DESCENT (SGD) DALAM MENGKLASIFIKASI TAHU BERFORMALIN,” Indones. J. Data Sci., vol. 3, no. 1, pp. 1–8, 2022, doi: https://doi.org/10.56705/ijodas.v3i1.42.
A. Winata, M. Dolok Lauro, and T. Handhayani, “PERBANDINGAN LSTM DAN ELM DALAM MEMPREDIKSI HARGA PANGAN KOTA TASIKMALAYA,” J. Ilmu Komput. dan Sist. Inf., vol. 11, no. 2, 2023, doi: https://doi.org/10.24912/jiksi.v11i2.26015.
J. Jtik, J. Teknologi, F. Kurnia, T. Putri, and A. D. Wowor, “IMPLEMENTASI ALGORITMA LONG SHORT TERM MEMORY DALAM PREDIKSI KONSENTRASI GAS METANA ( CH4 ) DI KOTA SALATIGA,” vol. 8, no. 2, 2024.doi: https://doi.org/10.35870/jtik.v8i2.1917
R. Anik and E. M. Afif, “ANALISIS TIME SERIES UNTUK MENENTUKAN MODEL TERBAIK PRODUK SONGKOK NASIONAL DI KABUPATEN GRESIK,” Pros. Semin. Nas. Mat. dan Ter., pp. 1–16, 2018.
S. Vincentius Riandaru Prasetyo*, Stefan Axel, Juan Timothy Soebroto, David Sugiarto and S. D. N. Ardi Winatan, “PREDIKSI HARGA EMAS BERDASARKAN DATA GOLD.ORG MENGGUNAKAN METODE LONG SHORT TERM MEMORY,” Sist. J. Sist. Inf., vol. 11, pp. 623–629, 2022.doi: https://doi.org/10.32520/stmsi.v11i3.1999
D. Z. Haq et al., “LONG SHORT TERM MEMORY ALGORITHM FOR RAINFALL PREDICTION BASED ON EL-NINO AND IOD DATA,” Procedia Comput. Sci., vol. 179, no. 2019, pp. 829–837, 2021, doi: https://doi.org/10.1016/j.procs.2021.01.071.
D. D. Pramesti, D. C. R. Novitasari, F. Setiawan, and H. Khaulasari, “LONG-SHORT TERM MEMORY (LSTM) FOR PREDICTING VELOCITY AND DIRECTION SEA SURFACE CURRENT ON BALI STRAIT,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 2, pp. 451–462, 2022, doi: https://doi.org/10.30598/barekengvol16iss2pp451-462.
A. Y. Labolo et al., “COMPARASI ALGORITMA FORECASTING SVM , K-NN DAN NN,” J. Sci. Soc. Res., vol. 4307, no. 2, pp. 289–299, 2022, [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR
I. I. Zulfa, D. C. R. Novitasari, F. Setiawan, A. Fanani, and M. Hafiyusholeh, “PREDICTION OF SEA SURFACE CURRENT VELOCITY AND DIRECTION USING LSTM,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 11, no. 1, p. 93, 2021, doi: https://doi.org/10.22146/ijeis.63669.
N. Ananda, H. S. Wicaksana, Y. G. Wijaya, and R. Hijazi, “HYPERPARAMETER TUNING LSTM SEBAGAI ESTIMATOR SENSOR RELATIVE HUMIDITY PADA AUTOMATIC WEATHER STATION BERBASIS SIMULATED ANNEALING,” Bul. Meteorol. Klimatologi, Dan Geofis., vol. 4, no. 1, pp. 35–43, 2023.
F. Nashrullah, S. A. Wibowo, and G. Budiman, “Investigasi Parameter Epoch Pada Arsitektur ResNet-50 Untuk Klasifikasi Pornografi,” J. Comput. Electron. Telecommun., vol. 1, no. 1, pp. 1–8, 2020, [Online]. Available: https://scholar.archive.org/work/5rwpasnveze2lj76wgj62lxaya/access/wayback/https://journal.ittelkom-sby.ac.id/complete/article/download/51/53
M. A. Rohman, Suhartono, and T. Chamidy, “BIDIRECTIONAL GRU DENGAN ATTENTION MECHANISM PADA ANALISIS SENTIMEN PLN MOBILE BIDIRECTIONAL GRU WITH ATTENTION MECHANISM ON SENTIMENT ANALYSIS OF PLN MOBILE,” Techno.com, vol. 22, no. 2, pp. 358–372, 2023.doi: https://doi.org/10.33633/tc.v22i2.7876
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