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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Renewable Energy Development IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan International Journal of Education, Science, Technology, and Engineering (IJESTE) International Journal of Recent Technology and Applied Science (IJORTAS) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Energi Baru dan Terbarukan JURNAL POLI-TEKNOLOGI Journal Research of Social Science, Economics, and Management International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Eko Adhi Setiawan
Departement Of Energy System Engineering, Faculty Of Engineering, University Of Indonesia
Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation

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Analysis of performance and economic value of thin film and monocrystalline photovoltaic systems in the tropical area of Jakarta Novagia Adita; Setiawan, Eko Adhi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 1 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i1.19

Abstract

Evaluation of PV performance is important as a development in supporting the government’s program to increase the renewable energy mix to reach 23% in 2025. This study aims to analyze the performance of thin film vs monocrystalline PV which has been operating in tropical areas in Jakarta. Then thin film and monocrystalline PV were simulated using PVsyst. Evaluation is carried out through a performance ratio analysis approach and the value of the degradation rate. The performance of thin film PV was 5% close to the simulation result but not for the monocrystalline silicon. The degradation rates for thin-film and mono-si were 0.93% and 2.07%, respectively. The degradation rates of PV are comparable to other studies that have been conducted in other countries with similar climates. Such a degradation rate value reduces the economic value of PV where the Levelized Cost of Energy (LCOE) value obtained decreases for thin films by 25% while for monocrystalline would change by 77%.
Electricity Demand Forecasting Using a Hybrid ARIMA and Ridge Regression Model Pradhipta Seno Parlinto; Eko Adhi Setiawan
Jurnal Energi Baru dan Terbarukan Vol 7, No 2 (2026): Mei 2026
Publisher : Program Studi Magister Energi, Sekolah Pascasarjana, Universitas Diponegoro, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jebt.2026.31328

Abstract

Effective energy system planning requires energy demand projections that are reliable, stable, and easy to implement, particularly under conditions of limited historical data and computational resources. However, many existing artificial intelligence–based forecasting approaches are highly complex, difficult to interpret, and time-consuming to develop, which reduces their practicality for students and researchers who aim to focus on solution-oriented energy system analysis. This paper proposes a simple yet reliable energy demand projection framework by combining statistical time series modeling and machine learning methods, namely Auto Regressive Integrated Moving Average (ARIMA) and Ridge Regression. The ARIMA model is employed to capture the temporal dynamics of energy consumption and to construct a business-as-usual (BAU) scenario based on historical trends. The ARIMA projections are subsequently used as inputs for the Ridge Regression model, which captures the multivariate relationships between energy demand and correlated socio-economic factors. The results indicate that ARIMA effectively represents historical consumption patterns but tends to produce conservative projections. In contrast, Ridge Regression provides more stable and robust estimates under conditions of high multicollinearity and limited sample size. The integration of these two methods results in an efficient, interpretable, and easily reproducible modeling framework. The proposed approach is intended to help students and researchers reduce the time required for energy demand forecasting, allowing them to focus more on solution development and sustainable energy system planning.
Co-Authors Abdul Rahim, Yang Ilya Akila Aditya Kurniawan Adrian Danar Wibisono Aiman Setiawan, Aiman Akhmad Herman Yuwono Budiyanto Budiyanto Budiyanto Budiyanto Eka Putri, Sonia Handini, Wike Ibrahim, Siti Nor Nadhirah Irma Suryanti Ismujiantoa, _ Khairiah Dewi Kurniawan Kurniawan Madsuha, Alfian Ferdiansyah Mohamad Zhafran Hussin Mohamad Zhafran Hussin Mohammed Ali Berawi Nofri Yenita Dahlan Novagia Adita Nur Iqtiyani Ilham Nyoman Suwartha Paranai Suhasfan Pradhipta Seno Parlinto Rudy Setiabudy Rudy Setiabudy Rudy Setiabudy1, Rudy Santoso, Hartono Budi Silalahi, Chandra Lima Sulaiman, Shahril Irwan Uno Bintang Sudibyo Uno Bintang Sudibyo, Uno Zafya Nadhira Affiandi Zainuddin, Hedzlin