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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.
Benchmarking Linear vs. Non-Linear Predictive Models for Energy Demand Forecasting in Electrical Motor-Driven Compressors Nazir, Humam; Izzuddin, Tarmizi Ahmad; 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
Publisher : IAES Indonesian Section

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

Abstract

In the LNG regasification plants, among the most energy-demanding components are the electrical motor driven compressors. Artificial Neural Networks (ANNs) and Extreme Gradient Boosting (XGBoost) feature superior flexibility in modelling nonlinear and dynamic compressor behaviour, unlike the conventional methods such as Multiple Linear Regression (MLR), which is limited by its assumption of linearity. This study focuses on data-driven techniques for compressor power consumption prediction, in which MLR, ANN and XGBOOST are compared. Here, real operational data collected from two Boil-off Gas (BOG) and two Regasification Terminal Export Compressors (RGTEC) were considered to develop and evaluate the models. The findings suggest that non-linear machine learning models provide superior predictive performance in comparison to those of the traditional linear approach. The highest prediction accuracy for most compressors is achieved using the ANN model, with a n R2 values of 94.6%, 99.64% and 99.64% for BOG-A, BOG-C and RGTEC-A, respectively. Meanwhile, the best performance was found for XGBOOST for RGTEC-B, with a R2 value of 94.17% and a significantly lower RMSE value. Contrary to that, MLR produced lower accuracy in several cases, particularly when subjected to complex operating conditions, in which linear assumptions were inadequate to capture system dynamics. In addition, this research features one of the first direct comparisons between linear and nonlinear models applied to real-time compressor data, unlike past research that depends on simulations or single-method analysis. It emphasizes the practical advantages of ANN and XGBoost for data-driven energy forecasting and operational optimizations in the gas industry.
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