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All Journal Mechatronics, Electrical Power, and Vehicular Technology Journal of Engineering and Technological Sciences Journal of Science and Applicative Technology Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Journal of Engineering and Technological Sciences
Justin Pradipta
Institut Teknologi Bandung
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Modelling and Identification of Oxygen Excess Ratio of Self-Humidified PEM Fuel Cell System Leksono, Edi; Pradipta, Justin; Tamba, Tua Agustinus
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 3, No 1 (2012)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (958.679 KB) | DOI: 10.14203/j.mev.2012.v3.39-48

Abstract

One essential parameter in fuel cell operation is oxygen excess ratio which describes comparison between reacted and supplied oxygen number in cathode. Oxygen excess ratio relates to fuel cell safety and lifetime. This paper explains development of air feed model and oxygen excess ratio calculation in commercial self-humidified PEM fuel cell system with 1 kW output power. This modelling was developed from measured data which was limited in open loop system. It was carried out to get relationship between oxygen excess ratio with stack output current and fan motor voltage. It generated fourth-order 56.26% best fit ARX linear polynomial model estimation (loss function = 0.0159, FPE = 0.0159) and second-order ARX nonlinear model estimation with 75 units of wavenet estimator with 84.95% best fit (loss function = 0.0139). The second-order ARX model linearization yielded 78.18% best fit (loss function = 0.0009, FPE = 0.0009).
Modelling and Identification of Oxygen Excess Ratio of Self-Humidified PEM Fuel Cell System Edi Leksono; Justin Pradipta; Tua Agustinus Tamba
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 3, No 1 (2012)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2012.v3.39-48

Abstract

One essential parameter in fuel cell operation is oxygen excess ratio which describes comparison between reacted and supplied oxygen number in cathode. Oxygen excess ratio relates to fuel cell safety and lifetime. This paper explains development of air feed model and oxygen excess ratio calculation in commercial self-humidified PEM fuel cell system with 1 kW output power. This modelling was developed from measured data which was limited in open loop system. It was carried out to get relationship between oxygen excess ratio with stack output current and fan motor voltage. It generated fourth-order 56.26% best fit ARX linear polynomial model estimation (loss function = 0.0159, FPE = 0.0159) and second-order ARX nonlinear model estimation with 75 units of wavenet estimator with 84.95% best fit (loss function = 0.0139). The second-order ARX model linearization yielded 78.18% best fit (loss function = 0.0009, FPE = 0.0009).
Data Driven Building Electricity Consumption Model Using Support Vector Regression FX Nugroho Soelami; Putu Handre Kertha Utama; Irsyad Nashirul Haq; Justin Pradipta; Edi Leksono; Meditya Wasesa
Journal of Engineering and Technological Sciences Vol. 53 No. 3 (2021)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2021.53.3.13

Abstract

Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption.
Pemodelan Manajemen Energi Microgrid pada Sistem Bangunan Cerdas FX Nugroho Soelami; Edi Leksono; Irsyad Nashirul Haq; Justin Pradipta; Putu Handre Kertha Utama; Aretha Fieradiella Pahrevi; Faizatuzzahrah Rahmaniah; Meditya Wasesa
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 4: November 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1528.314 KB) | DOI: 10.22146/jnteti.v9i4.488

Abstract

From the electricity system point of view, smart buildings can be seen as an integration of a microgrid electricity network that connects solar PV, storage system, and building load distribution. The operation condition of the microgrid needs to be evaluated and optimized to obtain efficient and reliable performance. This contribution presents an energy management modeling for the microgrid optimization process in a smart building system. The energy sources connected to the microgrid are solar PV, battery storage system, and the PLN (utility) grid. Combinations of load scenarios are evaluated, which consists of building a lighting system, water pump, dan HVAC system. The optimization goal is to find the optimal estimation of Self Consumption (SC) and Self Sufficiency (SS) values. A simulation result before the optimization shows that the system is operating with SC of 63.2% and SS of 96.32%. After the optimization, the values become SC = 84.68% and SS = 83.27%. Therefore, the amount of energy sourced from the Solar PV system is increased and the microgrid is working more optimally.
Peningkatan Kinerja Microgrid Bangunan Kampus dengan Simulasi Multi Skenario dan Analisis Sensitivitas Justin Pradipta; Koko Friansa; Irsyad Nashirul Haq; Edi Leksono; Hanafi Kusumayudha; Salsabila Regita; Mediya Wasesa
Journal of Science and Applicative Technology Vol 5 No 2 (2021): Journal of Science and Applicative Technology December Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v5i2.458

Abstract

Penelitian ini mengevaluasi kinerja microgrid cerdas dengan tujuan untuk meningkatkan ketersediaan pasokan listrik dan renewable fraction (RF). Evaluasi dilakukan dengan simulasi multi skenario yang mencakup produksi dan konsumsi energi. Simulasi dibagi tiga, yaitu skenario dasar, skenario uji, dan skenario rekomendasi. Skenario uji terdiri dari uji kapasitas sistem, penggantian komponen, dan analisis sensitivitas. Didapatkan dari skenario dasar bahwa ketersediaan pasokan listrik selama setahun telah terpenuhi, dengan RF 30,5%; cost of energy (CoE) Rp2.019/kWh; dan waktu otonomi baterai (WOB) 11,1 jam. Dari hasil analisis didapatkan beberapa rekomendasi berupa penggantian komponen baterai dan modul surya, penambahan kapasitas baterai, dan pengaturan batas state of charge (SoC) pada baterai untuk meningkatkan RF. Skenario rekomendasi tersebut berhasil meningkatkan ketersediaan pasokan listrik dan mencapai target dengan nilai WOB sebesar 37 jam dan RF sebesar 46,4% pada awal siklus hidup proyek; serta WOB sebesar 25,5 jam dan RF sebesar 29,1% pada akhir tahun ke 25, dengan CoE sebesar Rp6.448/kWh. Analisis sensitivitas operasi baterai lead-acid menunjukkan bahwa untuk mendapatkan RF maksimal rentang pengaturan SoC berada pada 0-20%. Sedangkan untuk baterai Li-Ion, rentang SoC adalah 0-25%.
Simulasi Energi dan Keekonomian Sistem Pembangkit Listrik Tenaga Surya (PLTS) untuk Fungsi Peak Load Shaving pada Bangunan di Lingkungan Kampus ITB Irsyad Nashirul Haq; Justin Pradipta; Muhamad Riezar Satria Sheba; Alief Widjoseno Damar Persada; F.X. Nugroho Soelami; Edi Leksono
Journal of Science and Applicative Technology Vol 5 No 1 (2021): Journal of Science and Applicative Technology June Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v5i1.449

Abstract

Pada paper ini, dilakukan simulasi produksi energi dan keekonomian dari sistem PLTS terintegrasi dengan jaringan listrik yang merupakan bagian dari proyek instalasi sistem smart microgrid di bangunan Center for Advanced Sciences (CAS) ITB. Terdapat dua sistem PLTS yang dianalisis yaitu sistem PLTS 40 kWp tanpa baterai, dan sistem PLTS 10 kWp dengan baterai yang terhubung ke beban kritis. Kedua sistem PLTS tersebut dirancang dengan tujuan peak load shaving yang dapat meminimalisir adanya ketidakseimbangan antara produksi energi surya dan permintaan listrik. Kedua sistem dianalisis dengan metoda simulasi berdasarkan aspek energi dan ekonomi menggunakan perangkat lunak PVsyst dan Homer. Dari hasil simulasi sistem PLTS 40 kWp didapatkan nilai performance ratio (PR) sebesar 0,833, renewable fraction (RF) sebesar 18,73%, dan cost of energy (COE) sebesar Rp 1.251,85/kWh yang mana nilai PR dan COE telah memenuhi target bisnis: PR > 0.8 dan COE < Rp 1.467,28/kWh tetapi tidak memenuhi target RF > 35%. Sementara dari hasil simulasi sistem PLTS 10 kWp didapatkan nilai PR sebesar 0,77, RF sebesar 44,38% hingga 52,19% pada rentang depth of discharge 20% - 80%, dan COE sebesar Rp 2.103/kWh hingga Rp 6.315/kWh pada rentang DoD 20% - 80% yang mana hanya nilai RF telah memenuhi target bisnis.
Pengembangan Pengontrol Tegangan Sistem Mikrogrid Cerdas Menggunakan Sistem Baterai Penyimpan Energi Putu Handre Kertha Utama; Irsyad Nashirul Haq; Edi Leksono; Justin Pradipta; M Daya Imannata; Timothius Pratama Tjahja
Journal of Science and Applicative Technology Vol 6 No 2 (2022): Journal of Science and Applicative Technology December Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v6i2.594

Abstract

A power outage on a conventional grid can cut the electricity supply to the entire load. In contrast, Microgrid (MG) can still supply at least the most critical local loads even though blackout occurs in the main grid. MG can also utilize renewable energy sources such as solar and wind energy to generate electricity. That is possible by the advancement of the battery energy storage system (BESS). The BESS able to maintains electricity supply to the load even in outages. The inverter on the SBPE also plays a role in stabilizing the MG output voltage by supplying or absorbing reactive power in the MG system. This paper focuses on the control development of the battery inverter primary controller. The droop control design utilizes the deadband around the nominal voltage. That becomes the improvement of the droop control method used in this study compared to the initial formulation of the droop method. The proposed method was then tested through simulation with four different scenarios. The BESS will operate in the voltage range 194.9V to 234.6V with a droop control deadband in the voltage range 198.0V to 231.0V. Based on the simulation results, the addition of SBPE with the MG scheme on the existing system can improve the quality of the voltage received by the load from 0.994p.u. to 0.997p.u. The simulation also shows that the load still gets a power supply even though there is a blackout on the main grid.
Development of Non-Intrusive Load Monitoring of Electricity Load Classification with Low-Frequency Sampling Based on Support Vector Machine Edi Leksono; Auditio Mandhany; Irsyad Nashirul Haq; Justin Pradipta; Putu Handre Kertha Utama; Reza Fauzi Iskandar; Rezky Mahesa Nanda
Journal of Engineering and Technological Sciences Vol. 55 No. 2 (2023)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2023.55.2.1

Abstract

Non-intrusive load monitoring (NILM) is a promising approach to provide energy consumption monitoring of electrical appliances and analysis of current and voltage data with less instrumentation. This paper proposes an electrical load classification model using support vector machine (SVM). SVM was chosen to keep the computational cost low and be able to implement an embedded system. The SVM model was utilized to classify the on/off state of air conditioners, light bulbs, other uncategorized electronics, and their combinations. It utilizes low-frequency sampling data captured every minute, or at a 0.0167 Hz rate. Utilization change in active and reactive power was used as a feature in the model training. The optimal kernel for the model was the radial basis function (RBF) kernel with C and gamma values of 88.587 and 2.336 as hyperparameters, producing a highly accurate model. In testing with real-time conditions, the model classified the on/off state of the electrical loads with 0.93 precision, 0.91 recall, and 0.91 f-score. The results of testing proved that the model can be applied in real time with high accuracy and with an acceptable performance in field implementation using an embedded system.
Pemodelan dan Simulasi MPPT pada Sistem PLTS Menggunakan Metode DNN Edi Leksono; Robi Sobirin; Reza Fauzi Iskandar; Putu Handre Kertha Utama; Mochammad Iqbal Bayeqi; Muhammad Fatih Hasan; Irsyad Nashirul Haq; Justin Pradipta
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 4: November 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i4.7931

Abstract

The maximum power point tracking (MPPT) feature in solar power plants is an essential function in increasing the efficiency of electricity production. The incremental conductance (InC) algorithm controls MPPT, aiming to maximize the output power of photovoltaic (PV) panels and increase the efficiency of the solar power plant system. Even though the InC algorithm is simple and practical, this algorithm tends to lack support in precise switching speeds, is sensitive to the measurement precision level, and is inadequate to eliminate power oscillations due to tight switching cycles. The deep neural network (DNN) algorithm has the potential to answer the challenges of MPPT dynamics. DNN’s learning capabilities enable the controller to better recognize the dynamics of shifts in maximum power values, thereby providing more appropriate contact actuation. The input for the DNN is the duty ratio produced by the InC algorithm. The DNN algorithm was implemented on three DC-to-DC power converter topologies, namely buck, boost, and buck-boost, to determine MPPT performance under standard tests and actual environmental conditions. DNN has demonstrated the ability to reduce oscillation effects, speed up steady-state time, and increase efficiency. In actual environmental conditions, the results showed that the buck converter consistently produced the highest power, followed by the boost and the buck-boost converters. Regarding performance efficiency, the buck converter achieved the highest efficiency at 94.58%, followed by the boost converter at 90.79%. Conversely, the buck-boost converter had the lowest performance efficiency, with an efficiency of 79.34%.
Adaptive Ensemble Learning for Enhancing Building Energy Consumption Prediction: Insights from COVID-19 Pandemic Energy Consumption Dynamics Handre Kertha Utama, Putu; Leksono, Edi; Nashirul Haq, Irsyad; Indrapraja, Rachmadi; Mahesa Nanda, Rezky; Friansa, Koko; Fauzi Iskandar, Reza; Pradipta, Justin
Journal of Engineering and Technological Sciences Vol. 57 No. 2 (2025): Vol. 57 No. 2 (2025): April
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.2.2

Abstract

Buildings account for approximately 40% of the total global energy consumption. Therefore, accurate prediction of building energy consumption is necessary to optimize resource allocation and promote sustainable energy usage. A key challenge in developing building energy consumption models is their adaptability to abrupt changes in consumption patterns owing to extraordinary events, such as the COVID-19 pandemic. Therefore, a two-layer ensemble-learning (EL) model incorporating sliding windows as input features is proposed. The model is a two-layer stacking EL consisting of two base learning methods: (1) support vector regression (SVR), and (2) random forest (RF). Temperature and humidity are included to account for the influence of weather conditions on energy consumption. The proposed model is deployed to forecast building energy consumption both before (November 2019) and during (May – October 2020) the COVID-19 pandemic and is compared with a single machine learning model. The results demonstrate that the EL model outperforms the SVR and RF methods, providing excellent prediction accuracy even during the pandemic when significant changes in energy consumption patterns occurred. The findings also highlight the effectiveness of sliding windows as input features for improving model adaptability. Additionally, the analysis reveals that temperature is more prominent than humidity for improving prediction accuracy.
Co-Authors Alief Widjoseno Damar Persada Aretha Fieradiella Pahrevi Arif M. Natawidjaja, Reyza Armanto Mangkuto, Rizki Auditio Mandhany Christian, Hadi Edi Leksono Edi Leksono Edi Leksono Edi Leksono Edi Leksono Estiyanti Ekawati F.X. Nugroho Soelami Faizatuzzahrah Rahmaniah Faniama, Virara Friansa, Koko FX Nugroho Soelami Hadi Christian Hanadi Hanafi Kusumayudha Handre Kertha Utama, Putu Haq , Irsyad N. Haq, Irsyad N. Haq Haq, Irsyad Nashirul Indrapraja, Rachmadi Koko Friansa Koko Friansa Koko Friansa Kristiawan Ariwibawa Leksono , Edi M Daya Imannata Mahesa Nanda, Rezky Meditya Wasesa Mediya Wasesa Mochammad Iqbal Bayeqi Muhamad Riezar Satria Sheba Muhammad Fatih Hasan N. Haq, Irsyad Nanda, Rezky Mahesa Nazuwatussya'diyah Nazuwatussya'diyah Puspita, Yumna Putu Handre Kertha Utama Putu Handre Kertha Utama Putu Handre Kertha Utama Reza Fauzi Iskandar Rezky Mahesa Nanda Robi Sobirin Romadhon, Rahmat Rozaq, M. Sya'banur Salsabila Regita Timothius Pratama Tjahja Tomayahu, Syafril Agustion Tomoyahu, Syafril Tua Agustinus Tamba Tua Agustinus Tamba Wasesa, Meditya