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All Journal Sinergi JOURNAL OF MECHANICAL ENGINEERING, MANUFACTURES, MATERIALS AND ENERGY Jurnal Energi Baru dan Terbarukan Eksergi: Jurnal Teknik Energi Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Grimsa Journal of Science Engineering and Technology
Saiful, Rifki
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Physics Other

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Journal : heca journal of applied sciences

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Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants Uhanto, Uhanto; Yandri, Erkata; Hilmi, Erik; Saiful, Rifki; Hamja, Nasrullah
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.219

Abstract

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.
Potential for Electrical Energy Savings in AC Systems by Utilizing Exhaust Heat from Outdoor Unit Hamja, Nasrullah; Yandri, Erkata; Hilmi, Erik; Uhanto, Uhanto; Saiful, Rifki
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.223

Abstract

This study explores the potential of utilizing waste heat from air conditioning systems, one of the largest consumers of electrical energy. Currently, most of the waste heat generated by outdoor units is typically released into the environment without being utilized, leading to missed energy-saving opportunities. This study analyzes the potential for improving electrical energy efficiency in air conditioning (AC) systems by harnessing this waste heat. Two primary approaches are evaluated: the first is the use of waste heat for domestic water heating, and the second is the conversion of heat into electrical energy using thermoelectric generators (TEG). The results of this research indicate that both methods have the potential to improve overall energy efficiency significantly. However, challenges related to conversion efficiency and integration of these technologies with AC systems require further, more specific studies. These findings are expected to contribute to more efficient and environmentally friendly cooling systems by optimizing technology and overcoming barriers to wider implementation.
Co-Authors Ariati, Ratna Arneta, Vanesa Cahya, Satiya Candra, Arief Dewi, Shanaya Rifiana Erkata Yandri Fitriani Fitriani Hamja, Nasrullah Hilmi, Erik Ibrahim, Riki Firmandha Lodewijk, Dewi Putriani Yogosara Muhammad Syukri Nur Nasrullah Nasrullah Rifiana Dewi, Shanaya Uhanto, Uhanto