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All Journal ASEAN Journal of Systems Engineering Jurnal Teknik Informatika (JUTIF) JRFES
Yuan, Anita
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ANALYSIS OF POTENTIAL FOR PHOTOVOLTAIC ROOFTOP USING HELIOSCOPE SOFTWARE (CASE STUDY: UNIVERSITAS KRISTEN IMMANUEL) Yuan, Anita; Setyowati, Emerita
JRFES (Jurnal Riset Fisika Edukasi dan Sains) Vol 11, No 1 (2024): Jurnal Riset Fisika Edukasi dan Sains
Publisher : Universitas PGRI Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22202/jrfes.2024.v11i1.8167

Abstract

Telah disimulasikan potensi PLTS Atap di Gedung Immanuel Music Center (IMC) menggunakan software Helioskop. Dipilih tiga lokasi atap yaitu Gedung IMC, Gedung Auditorium Musik Gereja (AMG) selatan, dan AMG utara. Selanjutnya parameter produksi energi listrik, performance ratio, shading, dan kWh/kWp dianalisis. Hasil simulasi menunjukkan bahwa nilai produksi energi listrik satu tahun mulai yang paling tinggi adalah di atap AMG Utara (31.560 MWh), atap AMG Selatan (29.290 MWh), dan atap IMC (20,400 MWh). Nilai performance ratio berturut-turut adalah 81.30 % (atap AMG utara ), 81.80 % (atap AMG selatan), dan 52.6 % (atap IMC). Gedung IMC berpotensi terkena bayangan sedangkan pada gedung AMG Utara dan Selatan bebas dari bayangan. Potensi nilai kWh/kWp secara berurutan dari yang paling tinggi adalah atap AMG utara (1.434,6) kemudian atap AMG selatan (1331,3) dan terakhir atap IMC (927,3). Dari hasil simulasi maka lokasi yang paling potensial untuk dipasang PLTS adalah atap Gedung AMG bagian utara.
ANALYSIS OF SOLAR ENERGY POTENTIAL TO ACHIEVE NEARLY ZERO ENERGY COMMUNITY. (CASE STUDY: MALIOBORO STREET, YOGYAKARTA). Yuan, Anita; Ridwan, Mohammad Kholid; Setiawan, Bakti
ASEAN Journal of Systems Engineering Vol 6, No 2 (2022): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v6i2.96176

Abstract

A number of concrete strategies developed at global, national and local level as a step forward in implementing the sustainability in the community. An interesting concept introduced by the European Commission about buildings that can reach nearly zero energy building with energy efficiency and use the integration of renewable energy sources. This concept then developed into nearly zero Energy Community (nZEC) at community level referring to a group of buildings. Along with the vision of the Malioboro area in the RTBL Final Report on the creation of an environment-friendly and sustainable Malioboro area, research on the potential of solar energy as a PV power source is conducted.This study applies a mapping of the distribution of solar energy potential using 3D building data analysis on Open Street Map (OSM) and Digital Elevation Model (DEM). ArcGIS Pro software was applied to analyze 3D buildings data and geospatial aspects. This study shows a negative correlation between building density and the average solar energy potential with a coefficient of determination R2=0.85. Coverage Degree shows that only by using a solar energy conversion system, the level of coverage by renewable energy can be achieved from 10.25% – 12.56%. However, this value is not enough to achieve energy independence through the nearly Zero Energy Community (nZEC) concept, so a way is needed to achieve it in the future. Significant planning and geometric parameters in relation to performance indicators provide insight as a reference for establishing solar energy-friendly urban planning and architectural design guidelines.
Hybrid Time-Series Approaches for PV Power Prediction: Evaluating SARIMAX and Generative Model Berutu, Sunneng Sandino; Zakaria, Immanuel Richie De Harjo; Yuan, Anita; Rahman, Mosiur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4955

Abstract

Forecasting the output power of photovoltaic (PV) systems is crucial in managing renewable energy efficiently and sustainably. The availability of historical data and environmental variables, such as temperature and humidity, greatly influences prediction accuracy. However, in practice, historical data is often incomplete due to technical constraints or limited monitoring infrastructure, which results in decreased prediction quality and system efficiency. To overcome these challenges, this study proposed a comparative approach between two predictive models, namely SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables) as a classical statistical model, and WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) as a generative deep learning model designed to handle incomplete data and capture nonlinear relationships. The datasets included PV power output from the monitoring system at Universitas Kristen Immanuel (UKRIM) Yogyakarta, along with temperature and humidity data from the Kalitirto weather station in Sleman, Yogyakarta. The research was conducted through several stages, namely: data collection, pre-processing, model training, and evaluation using MAE, MSE, RMSE, and MAPE metrics. The results show that the SARIMAX model using the Time-Series Cross-Validation (TSCV) achieves the best numerical performance (MAE = 0.085; RMSE = 0.145). However, this model fails to represent daily patterns realistically. In contrast, both the standard SARIMAX and WGAN-GP models are more consistent in representing seasonal patterns and daily fluctuations, even though their prediction errors were slightly higher in terms of numerical metrics. The findings advance scientific understanding of hybrid forecasting models and offer practical implications for improving energy reliability and decision-making in data-constrained environments.
Co-Authors Bakti Setiawan Emerita Setyowati Mohammad Kholid Ridwan Rahman, Mosiur Sunneng Sandino Berutu Zakaria, Immanuel Richie De Harjo