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All Journal International Journal of Electrical and Computer Engineering ASEAN Journal of Systems Engineering Teknofisika Journal of Engineering and Technological Sciences JPSE (Journal of Physical Science and Engineering) Jurnal Ilmiah Universitas Batanghari Jambi Jurnal Bakti Saintek: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Journal of Appropriate Technology for Community Services International Journal Of Science, Technology & Management (IJSTM)
Mohammad Kholid Ridwan
Department Of Nuclear And Physics, Faculty Of Engineering Universitas Gadjah Mada, Yogyakarta, Yogyakarta 55281, Indonesia.
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Physics Transportation Other

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Journal : ASEAN Journal of Systems Engineering

 1 
PERFORMANCE OF ROOFTOP PHOTOVOLTAIC SYSTEM WITH ADDITIONAL WATER COOLING SYSTEM Subur Priyono; Wahyu Wilopo; Mohammad Kholid Ridwan
ASEAN Journal of Systems Engineering Vol 4, No 2 (2020): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Improving solar power plant performance is considered quite important for existing and prospective users of rooftop solar power plants in Indonesia due to its unattractive economic value. One of the efforts to optimize the performance is the application of an additional cooling system on the plant's photovoltaic module. This study aimed to determine the effectiveness of temperature reduction of the applied cooling system on solar panel productivity. The research was performed on the existing rooftop solar power plant with a capacity of 3 kWp, located in Depok City with coordinates of 6°38'03.40" South Latitude and 106°82'03.49" East Longitude.The results showed that the additional water cooling system with a closed-loop pumping method on the installed solar module’s entire surface could improve the rooftop solar power plant performance with an average production increase of 15.7% in 7 days of study. Meanwhile, from an economic point of view, this cooling system installation payback period was 2 years.  
FORECASTING ANALYSIS ON ELECTRICITY DEMAND IN THE SPECIAL REGION OF YOGYAKARTA UNDER THE IMPACT OF THE COVID-19 PANDEMIC Feikal Aprieza; Mohammad Kholid Ridwan; Wahyu Wilopo
ASEAN Journal of Systems Engineering Vol 6, No 1 (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.v6i1.75149

Abstract

The COVID-19 pandemic as a global pandemic on 2020 has encouraged the Indonesian Government to establish pandemic response policies in many provinces. The policies that had been restricting mobility during the pandemic showed significant impacts in many aspects in the Special Region of Yogyakarta. A shifting pattern in electricity consumption can be seen as the growth of economic sectors in the GDP encountered contraction after the decline of community mobility. Electricity demand forecasting is required to analyze the impact of the COVID-19 pandemic by applying three scenarios, specifically an unlikely pandemic scenario or Business As Usual (BAU), moderate scenario (MOD), and optimistic scenario (OPT). Also, the household, industrial, business, social, and public sectors are analyzed in order to see the shifting pattern in electricity consumption through the scenarios that have been given. Energy modeling is conducted with Low Emission Analysis Platform (LEAP) software to analyze electricity demand forecasting from 2019 to 2030 based on the three scenarios. The results show that the electricity demand in 2030, according to BAU, MOD, and OPT scenarios, in the amount of 5,301.58 GWh, 4,489.11 GWh, and 4,648.12 GWh, respectively. According to the MOD and OPT scenarios, the electricity demands of the household and industrial sectors will increase relative to the BAU scenario. Meanwhile, according to both scenarios, the electricity demands of the business and social sectors will decrease. In the public sector, the MOD scenario shows the decline of electricity demand relative to the BAU scenario, while OPT scenario shows the opposite.
ON-GRID AND OFF-GRID RENEWABLE ENERGY DESIGN AS SWRO (SEAWATER REVERSE OSMOSIS) POWER FOR FRESHWATER NEEDS IN GILI TRAWANGAN (STUDI CASE: GILI TRAWANGAN) Robbani, Farisan; Ridwan, Mohammad Kholid; Darmawan, Arif
ASEAN Journal of Systems Engineering Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v9i1.77149

Abstract

Indonesia has 17,504 islands and one of them is Gili Trawangan. Gili Trawangan is one of the favorite tourist destinations in Indonesia which has an area of approximately 6 km2. In order to achieve the development of tourism islands, sufficient water and energy are needed in the area. The island's location in the middle of the sea makes it difficult to get freshwater and energy supplies.In this study, on-grid and off-grid simulations of renewable energy (solar, wind, and biomass) were carried out to determine the potential of integration energy as seawater desalination power as the need for freshwater in the area by calculating NPC (Net Present Cost), COE (Cost of Energy), operational costs, energy, and CO2 emissions. In the off-grid simulation, the lowest NPC and COE integration potentials were found at PV (Photovoltaic) capacities of 102 kW, 14 WT (Wind Turbine), 50 kW BG (Biogas Generator), and 50 kW DG (Diesel Generator) with a value of Rp 17,042,910,000 and Rp 1,587. The lowest operational costs and CO2 emissions are at the PV capacity of 209 kW, 8 WT, 50 kW BG, and 50 kW DG with a value of Rp 292,308,100 and 163 kg/year. In energy production, there is excess production and electrical energy at the PV capacity of 188 kW, 25 WT, 50 kW BG, and 50 kW DG with values of 2,301,533 kWh/year and 1,821,626 kWh/year. On-grid simulation results obtained the lowest NPC and NOE at 30 kW PV, 5 WT, and 50 kW BG with a value 2,961,782,000 and Rp. 200. Lowest operating costs, lowest CO2 emissions, largest electricity production, and best electricity trading at the PV capacity of 209 kW, 5 WT, and 50 BG with a value Rp 12,428,750, 84,000 kg/year, 882,455 kWh/year, 239,996 kWh, and 132,911 kWh.
SENSITIVITY ANALYSIS OF HYPERPARAMETER IN SOLAR ENERGY PREDICTION MODEL USING GRADIENT BOOSTING METHOD Ramadhan, Aska; Sopha, Bertha Maya; Ridwan, Mohammad Kholid
ASEAN Journal of Systems Engineering Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v9i1.78322

Abstract

Solar energy prediction is one alternative to handling unpredicted conditions of weather and solar radiation intensity. It could be the most important factor in achieving stability in electricity generation using solar energy resources. In making predictions, the use of machine learning models has been carried out by various methods, and in this study, the method used for the algorithm model is gradient boosting. In the modeling process using gradient boosting, several hyperparameter settings are needed. Hyperparameters have an important role in producing stable predictive patterns and can avoid overfitting or underfitting conditions. In this study, the accuracy and speed of prediction of the machine learning model with the gradient boosting approach, namely XGBoost and LightGBM, were analyzed in relation to setting the hyperparameter learning rate and max depth of the model's prediction pattern. The dataset used spans 6 months at a data resolution rate of every 5 minutes and includes meteorological data at the location point of Energy Laboratory UKRIM Yogyakarta as well as the output value of PLTS power and temperature panels onsite. Setting the hyperparameter learning rate in the highest and lowest conditions generates accuracy values with a difference of 2% and about the same prediction speed. With nMAE values of 2.84% and 1.35% and nRMSE values of 6.11% and 3.68%, respectively, the higher learning rate results in lower error values for both models. The XGBoost model shown tendency for overfitting and slower prediction speeds with the highest max depth setting. The prediction speed is faster at the lowest max depth condition, but the XGBoost and LightGBM models both exhibit underfitting.
Co-Authors Abram Perdana Adyta Pradana Nurrachman Ahmad Agus Setiawan Ahmad Agus Setiawan Ahmad Agus Setiawan Arif Darmawan Bayu Ardiyanto Bertha Maya Sopha Boni Sena Budy Wiryawan Defi Rizkasari Dyah R. Hizbaron Fadli Kasim Feikal Aprieza Fitri Yakub Hizbaron, Dyah R. Junun Sartohadi M. Yayan Adi Putra Maria E. I. Shrestha Nelidya Md Yusoff Nuri Aslami Ramadhan, Aska Resistentio Vembre Franika Robbani, Farisan Sasongko Pramono Hadi Sheikh Ahmad Zaki Shrestha, Maria E. I. Subur Priyono Syam Widiyanto Utami, Sentagi Sesotya Wahyu Wilopo Zulkifli Zulkifli