An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization.

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE's processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.

LC Passive Wireless Sensor System Based on Two Switches for Detection of Triple Parameters.

This paper presents the LC-type passive wireless sensing system for the simultaneous and independent detection of triple parameters, featuring three different capacitive sensors controlled by two mechanical switches. The sensor coil was connected with three different capacitors in parallel and two mechanical switches were in series between every two capacitors, which made the whole system have three resonant frequencies. The readout coil was magnetically coupled with the sensor coil to interrogate the sensor wirelessly. The circuit was simulated advanced design system (ADS) software, and the LC sensor system was mathematically analyzed by MATLAB. Results showed that the proposed LC sensing system could test three different capacitive sensors by detecting three different resonant frequencies. The sensitivity of sensors could be determined by the capacitance calculated from the detected resonant frequencies, and the resolution of capacitance was 0.1 PF and 0.2 PF when using the proposed sensor system in practical applications. To validate the proposed scheme, a PCB inductor and three variable capacitors were constructed with two mechanical switches to realize the desired system. Experimental results closely verified the simulation outputs.

Employing machine learning for advanced gap imputation in solar power generation databases.

This research evaluates the application of advanced machine learning algorithms, specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar energy generation databases and their impact on the size of green hydrogen production systems. The study demonstrates that the Random Forest model notably excels in harnessing solar data to optimize hydrogen production, achieving superior prediction accuracy with mean absolute error (MAE) of 0.0364, mean squared error (MSE) of 0.0097, root mean squared error (RMSE) of 0.0985, and a coefficient of determination (R2) of 0.9779. These metrics surpass those obtained from baseline models including linear regression and recurrent neural networks, highlighting the potential of accurate imputation to significantly enhance the efficiency and output of renewable energy systems. The findings advocate for the integration of robust data imputation methods in the design and operation of photovoltaic systems, contributing to the reliability and sustainability of energy resource management. Furthermore, this research makes significant contributions by showcasing the comparative performance of traditional machine learning models in handling data gaps, emphasizing the practical implications of data imputation on optimizing hydrogen production systems. By providing a detailed analysis and validation of the imputation models, this work offers valuable insights for future advancements in renewable energy technology.