Emrah Aslan, PhD – Assistant Professor, Computer Engineering, Mardin Artuklu University; Lecturer, Silvan Vocational School, Dicle University Türkiye
Yıldırım Özüpak, PhD – Assistant Professor, Electrical and Electronic Engineering, Dicle University, Türkiye

Series: Energy Science, Engineering and Technology; Computer Science, Technology and Applications
BISAC: COM051010; TEC031010; TEC009000
DOI: https://doi.org/10.52305/TJKH0287

This edited volume, Data-Driven Modeling and Explainable Machine Learning in Energy and Engine Systems, includes seven chapters that bring together recent methodological advances and practical applications of intelligent modeling techniques across a diverse range of engineering domains. Chapter 1 investigates short-term wind power prediction using real SCADA data, comparing traditional linear regression with recurrent neural networks to highlight the importance of temporal learning in capturing dynamic power generation behavior. Chapter 2 proposes a hybrid Prophet–XGBoost framework enriched with SHAP-based explainability, demonstrating how ensemble learning can simultaneously achieve high accuracy and interpretability at the inverter level. Chapter 3 presents a comprehensive acoustic emission–based bearing fault diagnosis framework using time-domain, time–frequency, and frequency-domain analyses. Chapter 4 focuses on aircraft engine NOx emissions. Chapter 5 introduces an Optuna-optimized XGBoost model for predicting power output in combined cycle power plants. Chapter 6 explores deep learning–based time series forecasting for nuclear electricity generation. Finally, Chapter 7 examines sensorless temperature estimation in permanent magnet synchronous motors.

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