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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Hashim, Haslenda
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Computer Science & IT Electrical & Electronics Engineering

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Determination of biomass energy potential based on regional characteristics using adaptive clustering method Alvianingsih, Ginas; Hashim, Haslenda; Jamian, Jasrul Jamani; Senen, Adri
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp46-55

Abstract

Determining the energy potential of biomass is the first step in selecting the most suitable and efficient energy conversion technology based on regional characteristics. The approach to estimating and determining biomass potential generally uses geospatial technology related to collecting and processing data about mapping an area. Unfortunately, this method is inadequate for simulating the interaction between variables, nor can it provide accurate predictions for the biomass supply chain. As a result, the results obtained from this method tend to be biased and macro, particularly in regions experiencing rapid land-use development. In this paper, the author has developed a clustering methodology with a fuzzy c-means (FCM) algorithm to determine biomass energy potential based on regional characteristics to produce data clusters with high accuracy. Grouping the characteristics of clustering-based areas involves grouping physical or abstract objects into classes or similar objects.
Comparing Neural Networks and Linear Regression for Power Prediction in Electrical Motor-Driven Compressors Izzuddin, Tarmizi Ahmad; Nazir, Humam; Zulkafli, Nur Izyan; Bin Sulaima, Mohamad Fani; Jali, Mohd Hafiz; Hashim, Haslenda; Jayiddin, Nur Saleha; Md Lasin, Azmi; Iskandar, M Tarmidzi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7777

Abstract

Electrical motor driven compressors are among the most energy-intensive components in LNG regasification plants, making accurate power consumption prediction is essential for cost reduction and emission control. Traditional methods, such as Multiple Linear Regression (MLR) are limited by their assumption of linearity, while Artificial Neural Networks (ANNs) offer greater flexibility in modelling nonlinear and dynamic compressor behavior. This study compares MLR and ANN models using real-time data from two Boil-off Gas (BOG) compressors and two Regasification Terminal Export Compressor (RGTEC) compressors. The results show that ANN consistently performs better than MLR. It achieved R² values of 98.3%, 99.9%, 99.9%, and 91.7% for the four compressors. In comparison, MLR reached R² values of 97.1%, 98.5%, 99.7%, and 64.1%. The ANN models also produced lower error magnitudes, including MAE and RMSE. This was especially true under unstable operating conditions when linear models failed to fit properly. Unlike previous studies that relied on simulations or single-method analysis, this research offers one of the first direct comparisons between linear and nonlinear models applied to real-time LNG compressor data. It highlights the practical benefits of ANN for data-driven energy forecasting and optimizing operations in the gas industry. The findings emphasize the value of data-driven methods, particularly neural networks, for improving energy forecasting and operational optimization in the gas sector.
Co-Authors Adri Senen Alvianingsih, Ginas Bin Sulaima, Mohamad Fani Iskandar, M Tarmidzi Izzuddin, Tarmizi Ahmad Jali, Mohd Hafiz Jamian, Jasrul Jamani Jayiddin, Nur Saleha Md Lasin, Azmi Nazir, Humam Zulkafli, Nur Izyan