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All Journal INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Vertex
Herjuna, Silvester Adi Surya
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Electricity demand forecasting in Ambon using machine learning techniques Herjuna, Silvester Adi Surya; Budisusila, Eka Nuryanto; Haddin, Muhammad
International Journal of Mechanical Computational and Manufacturing Research Vol. 13 No. 2 (2024): August: Mechanical Computational And Manufacturing Research
Publisher : Trigin Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/computational.v13i2.179

Abstract

This study aims to analyze the impact of electrical load forecasting using Artificial Neural Networks (ANN) to improve power supply reliability and efficiency in Ambon’s electric system. The objective is to develop a reliable forecasting model that supports effective energy management, helping to achieve operational excellence in terms of quality, safety, and cost-efficiency. A quantitative approach was utilized, gathering historical electricity load data from 2019 to 2024, alongside relevant environmental and temporal factors. The data were analyzed using ANN within a Python-based framework to predict future electricity demands accurately. The study employs a structured equation modeling to validate the forecasting model and its components. The findings reveal that the ANN model effectively predicts electrical loads with high accuracy, demonstrating substantial improvements in operational efficiency and energy cost reductions. The model’s ability to incorporate multiple input variables allows for nuanced understanding and prediction of load variations, thereby facilitating better resource allocation and strategic planning. This research contributes uniquely by applying ANN for electrical load forecasting in the context of Ambon’s electrical system, underscoring the integration of AI techniques in improving the operational efficiency of power utilities. The study extends the knowledge on the application of machine learning in the power sector by demonstrating how sophisticated forecasting models can significantly enhance energy management strategies
Performance Analysis of Wet Gas Flow in Up and Down Transmission Pipelines Herjuna, Silvester Adi Surya; Labriet, Andrieu; Akbar, Bima; Dimara, Johannes; Kaisiepo, Frans
Vertex Vol. 13 No. 2 (2024): June: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/apjc7f13

Abstract

This research examines the performance of wet gas flow in up and down transmission pipelines, addressing the critical challenges and dynamics associated with fluid behavior in varying orientations. Through extensive empirical analysis and modeling, the study identifies key factors influencing slugging, liquid accumulation, and flow efficiency. Findings reveal that upward transmission systems are particularly prone to slugging, leading to operational instability and increased energy demands, while downward flow systems benefit from gravitational assistance, resulting in enhanced reliability and reduced maintenance needs. Additionally, effective liquid management strategies and advanced monitoring technologies are essential for mitigating adverse effects and optimizing system performance. The research also addresses gaps in existing literature by providing new insights into flow management and environmental considerations, offering practical recommendations for pipeline design and operation. Ultimately, this study underscores the importance of ongoing research to refine understanding and improve practices in the transport of wet gas, contributing to the development of sustainable energy solutions.
Co-Authors Akbar, Bima Dimara, Johannes Eka Nuryanto Budi Susila Kaisiepo, Frans Labriet, Andrieu Muhamad Haddin