Md. Samin Safayat Islam’s research while affiliated with Rajshahi University of Engineering and Technology and other places

Integrated power systems encounter a multitude of challenges due to the variability observed in solar energy. As a result, it is requisite to make precise predictions regarding solar power. This study conducted a comparative analysis of various models including categorical boosting (CatBoost), random forest (RF), adaptive boosting (AdaBoost), gradient boosting (GB), extreme gradient boosting (XGBoost), and gated recurrent network (GRU). The objective of this paper is to determine the most accurate model for predicting global horizontal irradiance (GHI) in Canberra, Australia. This research used data collected at 60-minute, 30-minute, 15-minute, 10-minute, and 5-minute intervals throughout the year to ensure each model’s consistency in performance. As the number of samples increased in the dataset, each model’s performance improved significantly. This research made it quite evident that GRU outperformed all other models in producing the best results for this dataset.

The integration of drone technology, IoT, and AI can revolutionize smart town surveillance systems by reducing costs and improving response times to crime. The development of human-friendly drones and IoT technology enables automated city supervision, while the integration of AI allows for immediate reports and actions, overcoming human limitations. Drones can continuously monitor different areas, detecting crime hotspots and providing a substitute for city policies. This smart surveillance system can improve emergency management and ensure a regulated and competent surveillance management approach. Ultimately, this technology can enhance the safety and security of smart towns, making them more effective, efficient, and reliable. The framework of the suggested intelligent surveillance system relies on the utilization of real-time data collection and analysis, integrating drone technology, IoT devices, and AI. The research showcases the system’s efficacy in identifying and addressing criminal activities, making it an invaluable resource for law enforcement agencies. By combining these technologies, surveillance management can become more dependable and effective, ultimately contributing to the advancement of safer and smarter cities. The paper presents a comprehensive outline of the framework and its execution, providing a blueprint for future researchers and stakeholders to replicate and expand upon this endeavor. In this study, we present a superior surveillance system framework that is also implementable.

The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compared to contemporary ML models and traditional neural network models. This superiority is evident in 98% and 90% improvements in root mean square errors compared to feed-forward neural network and artificial neural network models, respectively. This research contributes to advancing accurate and efficient forecasting methods for PV power output, thereby enhancing the reliability and stability of power systems.