Time Series Analysis of Solar Power Generation Based on Historical Data and Irradiance Using the ARIMA Method

Time Series Analysis Pembangkit Listrik Tenaga Surya Berdasarkan Data Historis dan Iradiansi Menggunakan Metode ARIMA

Authors

  • Daisya Sopyan Universitas Alma Ata

DOI:

https://doi.org/10.57152/ijeere.v5i1.2014

Keywords:

Solar Power Plant, Performance Ratio, SARIMA, Forecasting, , Seasonal Analysis

Abstract

The demand for renewable energy in Indonesia continues to increase in line with the government's efforts to promote a sustainable energy transition. One of the rapidly growing technologies is On-Grid Solar Power Plants (PLTS), which rely on solar energy as their primary source. However, variations in solar irradiation and environmental factors cause fluctuations in the system's performance, potentially affecting its efficiency and reliability. Therefore, a robust method is needed to accurately predict system performance, supporting maintenance and operational optimization. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method as a time series analysis approach to predict the Performance Ratio (PR) of PLTS based on historical data and solar irradiation variables. SARIMA was chosen because stationarity tests revealed a significant seasonal pattern that conventional ARIMA models cannot effectively handle. By considering seasonal factors, SARIMA provides a more accurate estimation of PR trends and fluctuations. This research aims to detect potential anomalies early, identify recurring operational patterns, and improve PLTS system monitoring efficiency. Model evaluation results show that SARIMA has higher accuracy than ARIMA in capturing seasonal patterns in PR data. Implementing this model can assist PLTS operators in making more data-driven decisions, optimizing maintenance strategies, and ensuring the reliability of renewable energy systems. These findings contribute to the development of more efficient energy management strategies and support the sustainability of solar energy utilization in Indonesia.

References

I. Renewable Energy Agency, Renewable Energy Statistics 2022 Statisques D’Énergie Renouvelable 2022 Estaditicas De Energia Renovable 2022 About IRENA. 2022. [Daring]. Tersedia pada: www.irena.org

“Potensi Energi Baru Terbarukan (EBT) Indonesia,” 24 agustus 2008, 2008. Diakses: 5 Desember 2024. [Daring]. Tersedia pada: https://www.esdm.go.id/id/media-center/arsip-berita/potensi-energi-baru-terbarukan-ebt-indonesia

Y. Wicaksono, “Segmentasi Pelanggan Bisnis dengan Multi Kriteria Mengunakan K-Means,” Indonesian Journal of Business Intelligence (IJUBI), vol. 1, no. 2, hlm. 45, Mar 2019, doi: 10.21927/ijubi.v1i2.872.

Aldyfari dan Jefry, “Analisis Performance PLTS Rooftop 21.44 kWp Gedung D PT Indonesia Power PGU,” 2022.

D. Heksaputra, “Fuzzy Intelligence System for Employee Assesment: A Case Studi of XYZ University in Yogyakarta,” 2018. [Daring]. Tersedia pada: https://ejournal.almaata.ac.id/index.php/IJUBI

I. Amarulloh, “Peramalan Daya Listrik Jangka Pendek Pada Smart Grid Photovoltaic metode ARIMA dengan Pengaruh Suhu Pada Mode Hybrid,” 2021.

M. Al-Omary, R. Aljarrah, A. Albatayneh, dan M. Jaradat, “A Composite Moving Average Algorithm for Predicting Energy in Solar Powered Wireless Sensor Nodes,” dalam 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021, Institute of Electrical and Electronics Engineers Inc., Mar 2021, hlm. 1047–1052. doi: 10.1109/SSD52085.2021.9429440.

A. Akbar Harahap, “‘Avrillaila Akbar Harahap’ Perancangan Web E-Shop pada Toko Sandy dengan Menggunakan PHP dan MySQL,” 2018. [Daring]. Tersedia pada: https://ejournal.almaata.ac.id/index.php/IJUBI

S. Atique, S. Noureen, V. Roy, V. Subburaj, S. Bayne, dan J. MacFie, “Forecasting of total daily solar energy generation using ARIMA: A case study,” dalam 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019, Institute of Electrical and Electronics Engineers Inc., Mar 2019, hlm. 114–119. doi: 10.1109/CCWC.2019.8666481.

E. Chodakowska, J. Nazarko, ?. Nazarko, H. S. Rabayah, R. M. Abendeh, dan R. Alawneh, “ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations,” Energies (Basel), vol. 16, no. 13, Jul 2023, doi: 10.3390/en16135029.

M. Majidpour, H. Nazaripouya, P. Chu, H. R. Pota, dan R. Gadh, “Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System,” Forecasting, vol. 1, no. 1, hlm. 107–120, Des 2019, doi: 10.3390/forecast1010008.

A. Wisnu Widhi Nugraha, I. Rosyadi, dan F. Surya Tri Nugroho, “Desain Sistem Monitoring Sistem Photovoltaic Berbasis Internet of Things (IoT),” 2016.

F. Joisman Edas dan M. Ahmad Baihaqi, “Pengelolaan Daya pada Pembangkit Listrik Tenaga Surya dengan SCADA untuk Monitoring dan Kontrol Jarak Jauh,” 2024. [Daring]. Tersedia pada: https://ejournal.upm.ac.id/index.php/intro

Ardianto, Raharjo Budi Agus, dan Purwitasari diana, “Random Forest Regression Untuk Prediksi Produksi Daya Pembangkit Listrik Tenaga Surya,” Briliant, vol. 7, no. 4, 2022.

S. A. Solar Technology, “Performance ratio - Quality factor for the PV plant,” 2010.

G. Jain dan B. Mallick, “A Study of Time Series Models ARIMA and ETS,” 2017. [Daring]. Tersedia pada: https://ssrn.com/abstract=2898968

H. Sharadga, S. Hajimirza, dan R. S. Balog, “Time series forecasting of solar power generation for large-scale photovoltaic plants,” Renew Energy, vol. 150, hlm. 797–807, Mei 2020, doi: 10.1016/j.renene.2019.12.131.

Y. Listiana dan L. Prastiwi, “Model Matematika Keinggian Gelombang Perairan Pulau Bawean dengan Metode ARIMA,” Jurnal Matematika dan Pendidikan Matematika, vol. Vol. 3, 2018.

I. Aksan dan K. Nurfadilah, “Aplikasi Metode Arima Box-Jenkins Untuk Meramalkan Penggunaan Harian Data Seluler,” JOMTA Journal of Mathematics: Theory and Applications, vol. 2, no. 1, 2020.

Kastanja J. Arnold dan Tupalessy Johanis, “Peramalan Beban Listrik Kota Ambon Tahun 2016 - 2022,” SIMETRIK, vol. 7, no. 1, 2017.

Masarrang Maryantho, Yudaningtyas Erni, dan Naba Agus, “Peramalan Beban Jangka Panjang Sistem Kelistrikan Kota Palu Menggunakan Metode Logika Fuzzy,” EECCIS, vol. 9, no. 1, 2015.

I. Sungkawa, ; Ries, dan T. Megasari, “Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu Dalam Seleksi Model Peramalan Volume Penjualan Pt. Satriamandiri Citramulia,” 2011.

Razak Azhar Muhammad dan Riksakomara Edwin, “Peramalan Jumlah Produksi Ikan dengan Menggunakan Backpropagation Neural Network (Studi Kasus: UPTD Pelabuhan Perikanan Banjarmasin,” Jurnal Teknik ITS, vol. 6, no. 2337–3539, 2017.

H. Wibowo, Y. Mulyadi, dan A. G. Abdullah, “Peramalan Bebab Listrik Jangka Pendek Terklasifikasi Berbasis Metode Autoregressive Integrarated Moving Average,” 2012. [Daring]. Tersedia pada: http://jurnal.upi.edu/

S, D. Ruhiat, dan D. Dan Andiani, “Implementasi Model Autoregressive Integrated Moving Average (ARIMA) untuk Peramalan Jumlah Penumpang Kereta Api di Pulau Sumatera,” 2018.

D. Rusirawan dkk., “Research Collaboration of ITENAS Bandung - Indonesia and MATE Godollo - Hungary on the Photovoltaic Thematic Field: Achievements and Future Plan,” dalam E3S Web of Conferences, EDP Sciences, Feb 2024. doi: 10.1051/e3sconf/202448403011.

K. Katterbauer, A. F. Marsala, V. Schoepf, dan E. Donzier, “A novel artificial intelligence automatic detection framework to increase reliability of PLT gas bubble sensing,” Journal of Petroleum Exploration and Production, vol. 11, no. 3, hlm. 1263–1273, Mar 2021, doi: 10.1007/s13202-021-01098-1.

W. Mckinney, “Python for Data Analysis,” 2022. [Daring]. Tersedia pada: www.allitebooks.com

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Published

2025-07-21

How to Cite

[1]
D. Sopyan, “Time Series Analysis of Solar Power Generation Based on Historical Data and Irradiance Using the ARIMA Method: Time Series Analysis Pembangkit Listrik Tenaga Surya Berdasarkan Data Historis dan Iradiansi Menggunakan Metode ARIMA”, IJEERE, vol. 5, no. 1, pp. 61-78, Jul. 2025.

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Table of Contents IJEERE