M. Masud

Design of a Novel Hybrid Switched-Inductor/Switched-Capacitor DC-DC Step-Up Converter

In this paper, a new topology of a step-up DC converter is presented for renewable energy applications, yielding a high step-up conversion ratio and reduced voltage stress across the switches. The proposed converter uses a hybrid switched-inductor and switched-capacitor cell to achieve the high voltage gain. Additional...

Lite-BiAttn: A Lightweight CNN-BiLSTM Attention Model for Accurate Solar Radiation Forecasting

Reliable short-term prediction of global horizontal irradiance (GHI) is essential for maximizing photovoltaic (PV) power generation, improving grid reliability, and enabling efficient integration of renewable energy sources. This study proposes Lite-BiAttn, a lightweight hybrid deep learning framework that combines one...

Integrated Data-Driven Framework for Rooftop PV Impact Assessment in Distribution Networks Using Net-Load Forecasting and Hosting Capacity Analysis

The increasing use of rooftop photovoltaic (PV) systems in distribution networks can lead to operational challenges, including feeder overloading, reverse power flow, fluctuating net-load characteristics, and future hosting-capacity constraints. This research presents an integrated data-driven approach for the holistic...

A Coordinated Control-Based Power Management Strategy for a Hybrid Solar–Wind–Battery Integrated Standalone DC Microgrid for Rural Electrification

Standalone DC microgrids offer a promising solution for providing reliable and sustainable electricity to remote communities in developing countries. However, the intermittent nature of solar and wind resources, combined with continuously varying load demand, presents considerable operational challenges in maintaining...

Attention-Enhanced CNN–BiLSTM Framework with Temporal Feature Engineering for Global Horizontal Irradiance Forecasting

The proposed CNN–BiLSTM–Attention framework performed better than conventional benchmark models of machine learning and deep learning, achieving a Root Mean Square Error of 0.088 and Seasonal and atmospheric-condition evaluations also showed consistent performance across different seasonal and atmospheric conditions.

Solar-Powered IoT–ML Framework for Energy-Aware Crop Suitability Prediction

By integrating solar-powered IoT infrastructure with ML-based analytics, class-imbalance handling, and computational-efficiency evaluation, the proposed framework offers a practical and scalable solution for energy-aware suitability assessment in resource-constrained farming environments.

RTT NTN-Based Positioning With a Single Satellite in 6G: A Geometric Framework

The results show that accurate positioning can be achieved with as few as two RTT measurements, while additional measurements improve accuracy, particularly at lower LEO altitudes, and integration with the 5G NR signaling is discussed in detail, showing that only minor extensions are required.

Particle Swarm Optimization-Based Feature Weighting and Early-Cycle Remaining Useful Life Prediction of Lithium-Ion Batteries

A machine learning framework using particle swarm optimization (PSO) to perform hyperparameter tuning and feature optimization to improve lithium-ion battery RUL prediction and offers a highly interpretable and effective solution for predicting lithium-ion battery RUL.

Causal trust aware federated multi agent reinforcement learning for 6G edge networks

Solar photovoltaic fault classifications under normal and shading conditions using machine learning models

This study suggested PV monitoring systems with lower maintenance costs and energy losses, and an accurate and efficient classifier for PV defects using normal and shading condition based on advanced ML techniques like Random Forest, K-Nearest Neighbors, Decision Tree, and SVM are proposed.