Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

In this research, an automated yaw drive mechanism for a horizontal axis wind turbine was utilized to adjust the nacelle position to face the wind direction. Such wind turbine’s potential power extraction efficiency depends on the proper yaw control. In this study, a servo mechanized, automated control system has been developed and analyzed, applicable for small off-grid HAWT (Horizontal Axis Wind Turbine). To demonstrate the technical feasibility and superiority of the proposed solution, a mathematical model along with sensory calibration was developed and tested using an analog potentiometer. The system responses were recorded in an Internet of Things (IoT) server, and the mathematical model was validated by comparing the calibrated values with the experimental values, which showed about 97% accuracy. This system architecture uses a low-powered NodeMCU v.2 LoRa (Long Ranged) wireless controller for data transmission, and the ‘Thingspeak’ server, along with the MATLAB coding environment, has been utilized for data analytics and system response correction.