Explore the Research Focus and Publications
This section highlights Akteruzzaman’s research vision and guiding principles, offering a compelling overview of their scholarly objectives and the pivotal publications shaping their impactful contributions.
Discover Akteruzzaman’s Research & Publications
Explore a curated selection of research papers and publication insights by Akteruzzaman, emphasizing critical and sustainable energy.
Renewable Energy and Grid Stability
ACO surpasses GA, PSO, and SA in microgrid energy management, optimizing cost, renewable use, stability, and demand response.
Solar Cells: Efficiency & Commercial Potential
Perovskite solar cells Enhanced efficiency, scalability, sustainability, and commercialization potential.
Perovskite & Tandem Solar Cells: High-Efficiency Advances
Perovskite solar cells: >25% efficiency, better stability, optimized light management, and commercialization potential.
Predictive Model for Solar PV Performance Analysis
Model predicts solar PV performance using environmental, technical, and economic factors from 524 PRISMA-guided studies.
Insightful Research Contributions by Akteruzzaman
Discover detailed summaries and analyses of key research papers that demonstrate impactful advancements in critical and sustainable energy.
Regression-Based Lithium-Ion Battery Lifespan Prediction
Accurately predicting the lifespan of lithium-ion batteries is essential for effective battery management systems, promoting reliability and timely maintenance. Traditional methods often struggle with precision in early degradation phases. This study proposes a predictive framework using advanced regression techniques to improve battery performance and sustainability. Drawing from a dataset of 15,065 samples from NMC-LCO 18650 batteries, it prioritizes thorough data preprocessing feature extraction, normalization, and PCA for dimensionality reduction to boost model accuracy. Models including Linear Regression, Random Forest, Dense Neural Networks (Dense NN), and k-Nearest Neighbors (KNN) were trained and assessed via Mean Absolute Error (MAE) and R-squared (R²) metrics. KNN and Random Forest excelled in accuracy for lifespan estimation. The framework’s real-world utility is shown through Dense NN deployment with TensorFlow Serving for Remaining Useful Life (RUL) prediction, enabling proactive lifecycle management and advancing energy efficiency and sustainability in battery technologies.
Solar PV Model for Sustainable Energy Optimization
This study investigates the real-world deployment and validation of a solar photovoltaic (PV) performance model for sustainable energy optimization. Unlike theoretical or simulation-focused prior research, it applies the model across diverse grid-connected PV installations, integrating real-time monitoring of irradiance, temperature, conditions, output, and losses. Over 12 months, model predictions matched field data with <5% mean absolute percentage error, identifying inefficiencies like temperature derating, mismatch losses, and inverter clipping. Coupling with adaptive controls improved efficiency, reduced curtailment, and enhanced reliability.
Sustainable Energy Trends Python-Driven Insights
This paper analyzes the transition of the Netherlands’ electricity generation from fossil fuels to renewable sources, focusing on solar and wind energy. Using Python for data preprocessing, trend analysis, and visualization, the study identifies key temporal patterns in energy production. Results show that by 2023, renewable energy supplied nearly 50% of national electricity demand, with solar and wind contributing approximately 17.6% and 15%, respectively. These gains were largely driven by large-scale photovoltaic installations and offshore wind projects. The paper also discusses challenges of renewable intermittency, grid integration, and policy implications. The findings provide insights for policymakers and energy planners, while offering a data-driven framework that can be applied to other regions pursuing sustainable energy transitions.
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