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Arif Afandi
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FRONTIER ENERGY SYSTEM AND POWER ENGINEERING Electrical Engineering, Universitas Negeri Malang Jl. Semarang 5, Malang 65145, Jawa Timur, Indonesia
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Kota malang,
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Frontier Energy System and Power Engineering
Published by Universitas Negeri Malang
ISSN : -     EISSN : 27209598     DOI : http://dx.doi.org/10.17977/um049v2i1p1-6
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy
Frontier Energy System and Power Engineering, FESPE, is an International Journal registered at e-ISSN: 2720-9598. FESPE is officially published by Electrical Engineering, State University of Malang, Indonesia. This journal is the Peer Review and Open Access International Journal, published twice a year in January and July relating to the broad scope of the Energy System and Power Engineering. FESPE provides a flagship forum for academics, researchers, industry professionals, engineers, consultants, managers, educators, and policymakers who work in engineering to contribute and disseminate new innovative works in energy systems, power engineering, and other related themes.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 5 Documents
 1 
Search results for , issue "Vol 5, No 2 (2023): July" : 5 Documents clear
Optimization of Double Exponential Smoothing Using Particle Swarm Optimization Algorithm in Electricity Load Vivi Aida Fitria; Arif Nur Afandi; Aripriharta Aripriharta; Danang Arbian Sulistyo
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p58-64

Abstract

Electricity load forecasting plays a critical role in ensuring the efficient allocation of resources, maintenance optimization, and uninterrupted power supply. The double exponential smoothing (DES) method is widely used in forecasting time series data due to its adaptability and robustness, particularly in handling linear trends without seasonal patterns. However, determining the optimal value of the alpha parameter in DES is crucial for accurate forecasting results. This study proposes the use of the Particle Swarm Optimization (PSO) algorithm to optimize the alpha parameter in DES for electricity load forecasting. PSO is a computational method that iteratively improves candidate solutions by moving particles in the search space based on simple mathematical formulas. By optimizing the alpha parameter using PSO, we aim to enhance the accuracy of short-term electricity load forecasts. Our results demonstrate that the PSO-optimized DES approach achieves a Mean Absolute Percentage Error (MAPE) of 2.89% and an accuracy of 97.11%, indicating significant improvements in forecasting performance. While the PSO algorithm provides promising results, future research may explore the application of other metaheuristic algorithms, such as the whale or orca algorithms, to further enhance the optimization of DES parameters for electricity load forecasting. This study contributes to the advancement of forecasting techniques in the power industry, facilitating more efficient power generation and distribution planning.
The Enhanced Self-Lift Luo Converter with Qhbm for Maximum Power Extraction on PV Charging Station
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p65-80

Abstract

This journal presents the development of an innovative algorithm for Maximum Power Point Tracking (MPPT) utilizing the Enhanced Self Lift Luo Converter (ESLLC) developed through Queen Honey Bee Migration (QHBM). The QHBM used for MPPT employs a queen-based decision-making approach to determine the optimal point on solar panels. The queen continuously searches for the Maximum Power Point (MPP), and upon locating it, ceases tracking and starts building a nest. Once the nest is established, the queen resumes the search for MPP. The testing simulation evaluates computing speed, durability, and MPP's margin errors. MATLAB/Simulink is employed for verification. The simulation results demonstrate that the QHBM surpasses other algorithms like PSO, P&O, and FLC in terms of computing speed, durability, and MPP margin errors. The QHBM-based MPPT exhibits superior responsiveness to changes in irradiation and temperature compared to alternative algorithms. This proposed algorithm effectively adapts to varying environmental conditions that influence irradiation and temperature changes. Consequently, the suggested algorithm holds significant promise for practical implementation in dynamic environmental settings.
Temperature Monitoring Efficiency With Internet of Things-Based Temperature Sensor Variations IGN Sangka; IGS Widharma; IN Sunaya; IM Sajayasa; IGP Arka
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p81-87

Abstract

This research was conducted in the lab. Automation to measure temperature by several temperature sensors monitored through Internet of Things (IoT) technology. The method used is a quantitative method with experiments. The temperature monitoring system with a variety of temperature sensors based on the Internet of Things (IoT) can work well. The difference in temperature measurements taken through the IoT simulation within 1 hour between the sensors used has a small value, an average of 0.07 Celsius. This means that the precession level of each sensor is relatively good. The need for calibration on the sensor in order to obtain the best measurement results. Noting the results of the above analysis, it is recommended that users conduct a more in-depth study to obtain a more appropriate (actual) value. This analysis can also be taken into consideration for users to develop IoT-based monitoring systems in the future.
Dynamic System Modeling of Concentrated Electrical Energy Provision with Reducing CO2 Emissions in East Nusa Tenggara Rusman Sinaga; Frans Mangngi; Purnawarman Ginting
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p41-49

Abstract

Electric Power Generation (EPG) sourced from Fossil Energy (FE) has triggered an increase in CO_2 emissions in the atmosphere resulting in global warming which has a systemic impact on climate change. Whatever form of mechanism to reduce the increase in earth's temperature must be implemented. One way is to use a Solar Water Pump (SWP). This research aims to determine the use of EPG in East Nusa Tenggara (ENT) and find the amount of CO_2 emissions that can be reduced using SWP. This research uses dynamic modeling methods. The research results show that the production of electrical energy sourced from FE until 2030 is 2.270.656 MWh (87.77%), while the production of electrical energy sourced from RE is 316.441 MWh (12.23% ). EPG sourced from FE in ENT results in CO_2 emissions at 22.603.641 tons. According to survey results, conversion of FEPWP to SWP can reduce CO_2 emissions at 424.942 tons.
Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN) Aji Prasetya Wibawa; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Agung Bella Putra Utama; Jehad A.H. Hammad
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p50-57

Abstract

Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In testing with z-score normalization, it showed a performance of MAPE of 10.6120%, RMSE of 0.7648, and R2 of 0.4142.

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