ANALYSIS OF FORECAST OF RENEWABLE ENERGY DEVELOPMENT IN NORTH SUMATRA USING ANFIS
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Yoga Tri Nugraha(1*)
Universitas Al-Azhar Medan
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Puan Maharani Simanjuntak(2)
Universitas Prima Indonesia
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Muhammad Irwanto(3)
Universitas Prima Indonesia
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Rizkha Rida(4)
Universitas Al-Azhar Medan
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M.A. Othman(5)
Universiti Teknikal Malaysia Melaka
(*) Corresponding Author
Keywords:
Renewable Energy Development, ANFIS, North Sumatra
Abstract
The transition towards renewable energy sources is critical for sustainable development, particularly in regions like North Sumatra, Indonesia, where energy demand is increasing rapidly. This research presents an analysis of the forecast of renewable energy development in North Sumatra using the ANFIS. The analysis begins with data collection and preprocessing, incorporating historical data on energy consumption, renewable energy installations, population growth, economic indicators, and environmental factors. ANFIS models are then developed and optimized to capture the complex relationships between these variables and forecast renewable energy trends accurately. Model validation and performance evaluation techniques ensure the reliability of the forecasted outcomes. The results of the calculations conducted using the ANFIS method obtained an error value of 0,000201092% and has a Forecast of Renewable Energy Development in North Sumatra in 2028 of 160.44 MW.
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References
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[2] C. Ghenai, O. A. A. Al-Mufti, O. A. M. Al-Isawi, L. H. L. Amirah, and A. Merabet, “Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system (ANFIS),” J. Build. Eng., vol. 52, p. 104323, 2022, doi: https://www.sciencedirect.com/science/article/abs/pii/S2352710222003369
[3] A. Zaaoumi, A. Bah, M. Alaoui, A. Mechaqrane, and M. Berrehili, “Application of artificial neural networks and adaptive neuro-fuzzy inference system to estimate the energy generation of a solar power plant in Ain Beni-Mathar (Morocco),” in 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2018, pp. 1–6. doi: 10.1109/ECAI.2018.8679015.
[4] H. Tindra, I. D. Sara, and R. Adriman, “Daily Peak Load Forecast of Banda Aceh City Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method,” in 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), 2023, pp. 238–243. doi: 10.1109/COSITE60233.2023.10249581.
[5] A. Azeem, I. Ismail, S. M. Jameel, and V. R. Harindran, “Electrical Load Forecasting Models for Different Generation Modalities: A Review,” IEEE Access, vol. 9, pp. 142239–142263, 2021, doi: 10.1109/ACCESS.2021.3120731.
[6] D. Li, G. Sun, S. Miao, Y. Gu, Y. Zhang, and S. He, “A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence,” Int. J. Electr. Power Energy Syst., vol. 137, p. 107627, 2022, doi: 10.1016/j.ijepes.2021.107627.
[7] Y. T. Nugraha and M. Irwanto, “Modelling Demand for Energy Sources as Alternative Energy in the Province of North Sumatra,” J. Renew. Energy, Electr. Comput. Eng., vol. 2, no. 2, p. 84, 2022, doi: 10.29103/jreece.v2i2.9278.
[8] I. M. El-Hasnony, S. I. Barakat, and R. R. Mostafa, “Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment,” IEEE Access, vol. 8, pp. 119252–119270, 2020, doi: 10.1109/ACCESS.2020.3005614.
[9] P. Almaleck, S. Massucco, G. Mosaico, M. Saviozzi, P. Serra, and F. Silvestro, “Electrical consumption forecasting in sports venues: A proposed approach based on neural networks and ARIMAX Models,” Sustain. Cities Soc., vol. 100, p. 105019, 2024, doi: 10.1016/j.scs.2023.105019.
[10] J. Peng, S. Gao, and A. Ding, “Study of the short-term electric load forecast based on ANFIS,” in 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2017, pp. 832–836. doi: 10.1109/YAC.2017.7967525.
[11] Y. Tri Nugraha, M. F. Zambak, and A. Hasibuan, “Perkiraan Konsumsi Energi Listrik Di Aceh Pada Tahun 2028 Menggunakan Metode Adaptive Neuro Fuzzy Inference System,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 1, pp. 104–108, 2020, doi: 10.24114/cess.v5i1.15624.
[12] Y. T. Nugraha, K. Ghabriel, and I. F. Dharmawan, “Implementasi ANFIS Dalam Prakiraan Konsumsi Energi Listrik Di Kota Medan Pada Tahun 2030,” RELE (Rekayasa Elektr. Dan Energi) J. Tek. Elektro, vol. 4, no. 1, pp. 55–59, 2021, doi: 10.30596/rele.v4i1.7826.
[13] A. Qazi et al., “Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions,” IEEE Access, vol. 7, pp. 63837–63851, 2019, doi: 10.1109/ACCESS.2019.2906402.
[14] A. A. Zakri, M. W. Mustafa, and I. Tribowo, “ANFIS Design Based on Prediction Models for The Photovoltaic System,” in 2019 International Conference on Sustainable Information Engineering and Technology (SIET), 2019, pp. 234–239. doi: 10.1109/SIET48054.2019.8986133.
[15] T. İnan and A. F. BABA, “Prediction of Wind Speed Using Artificial Neural Networks and ANFIS Methods (Observation Buoy Example),” in 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 2020, pp. 1–5. doi: 10.1109/ASYU50717.2020.9259894.
[16] D. K. Ghose, K. Tanaya, A. Sahoo, and U. Kumar, “Performance Evaluation of hybrid ANFIS model for Flood Prediction,” in 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), 2022, vol. 1, pp. 772–777. doi: 10.1109/ICACCS54159.2022.9785002.
Published
2024-04-30
How to Cite
[1]
Y. T. Nugraha, P. M. Simanjuntak, M. Irwanto, R. Rida, and M. Othman, “ANALYSIS OF FORECAST OF RENEWABLE ENERGY DEVELOPMENT IN NORTH SUMATRA USING ANFIS”, JME, vol. 13, no. 1, pp. 27 - 36, Apr. 2024.
Section
Articles
Copyright (c) 2024 Yoga Tri Nugraha
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