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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics
Siti Azirah Asmai
Universiti Teknikal Malaysia Melaka
Computer Science & IT Engineering

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AncDE with gaussian distribution for numerical optimization problem Siti Khadijah Mohd Salleh; Siti Azirah Asmai; Zuraida Abal Abas; Abdul Samad Shibghatullah; Diarmuid O'Donoghue
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.258

Abstract

This work is introducing an enhanced Differential Evolution (DE) called AncDE. This proposed algorithm is using an additional population from the current generation and located it as ancestor. There are two parameter controllers to manage the selection of ancestor vector; aup for selection frequency and arp for age of selection. In this work we were applying Gaussian distribution on aup and we tested it on CEC 2015 Numerical Optimization Problem. Standard Differential Evolution will act as the benchmark. The result shows that AncDE with Gaussian approach has produced better result than standard DE.
Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability Nur Diana Izzani Masdzarif; Siti Azirah Asmai; Yogan Jaya Kumar; Muhammad Hafidz Fazli Md Fauadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3103-3119

Abstract

Technical energy loss estimation in power distribution systems is essential for improving operational efficiency and cost-effectiveness. However, distribution-level datasets are often failed to cope with the nonlinear behavior and sparse, low-resolution data typical in modern grid environments. This study proposes an interpretable artificial intelligence (AI) framework based on least absolute shrinkage and selection operator (LASSO) regression integrated with Shapley additive explanations (SHAP) to estimate and explain technical energy losses at the feeder level. An exploratory multicollinearity assessment using correlation analysis and variance inflation factor (VIF) revealed severe redundancy among operational variables, justifying the adoption of L1-regularized regression. Hyperparameter tuning via LassoCV identified an optimal regularization parameter, resulting in strong predictive performance. Comparative evaluation with nonlinear models, including random forest and gradient boosting, demonstrated that LASSO achieves competitive or superior generalization performance while preserving interpretability. Feature importance analysis and SHAP-based explanations confirmed that operational loading variables particularly infeed energy, load factor, and maximum demand are the dominant drivers of technical losses. SHAP dependence and interaction analyses further revealed context-dependent behavior among correlated predictors, enriching interpretability beyond coefficient-based rankings. The results demonstrate that regularized linear modeling, when combined with explainable AI techniques, provides a robust, transparent, and practically deployable solution for technical loss estimation in distribution networks.
Co-Authors Abdul Samad Shibghatullah Diarmuid O'Donoghue Muhammad Hafidz Fazli Md Fauadi Nur Diana Izzani Masdzarif Siti Khadijah Mohd Salleh Yogan Jaya Kumar Zuraida Abal Abas