This paper outlines a novel design of low-cost, portable, fast, and precise Current-Voltage Curve Tracer (IVCT) with automated parameter extraction for high power rated Solar Photovoltaic (SPV) modules to effectively and efficiently determine the outdoor operating status of SPV power generators. The developed IVCT is based on a Raspberry Pi microprocessor, a super-capacitive load, heat sinkable discharge resistances, and sensors with high sensitivity and resolution for measuring light irradiance, module temperature, current, and voltage. The proposed Outdoor Test Facility (OTF) consists of an Current-Voltage ( $I-V$ ) and a Power-Voltage ( $P-V$ ) curve tracer that uses a dynamic loading supercapacitor to safely and quickly scan the SPV module performance characteristics under real-world operating conditions. It also helps to achieve uniform sampling with better data accuracy. It uses Raspberry Pi as a central processing unit for low-cost data acquisition, data logging, and data computation. Furthermore, results from on-field testing of various small-scale SPV modules show that the $I-V$ tracer can acquire higher-resolution characteristics curves and perform accurate model parameter recognition in real-time. Proposed IVCT can measure individual SPV modules without altering the electrical interconnection circuit, and the operating point can be shifted to 20 $A$ and $45~V$ in few seconds. The proposed system recomposes the SPV module $I-V$ characteristics based on this variance, with accuracies of 1 to 3% for the region near maximum power.

Managing various aspects of the Supply Chain (SC) has become increasingly challenging in today’s complex business landscape. To improve profitability, boost sales, and enhance customer satisfaction, it is crucial to explore future possibilities by adjusting key relational parameters. However, traditional forecasting methods often fail to provide accurate insights and are time-consuming. These limitations can be overcome using Artificial Intelligence (AI) algorithms such as Machine Learning (ML) and Deep Learning (DL). CatBoost algorithm is an ensemble-based ML model that can handle categorical variables effectively in its architecture, whereas other ML and DL models fail and require explicit encoding techniques. In this study, a predictive modeling approach using CatBoost to optimize supply chain processes using a mathematical approach was proposed. CatBoost evaluates the model on an e-commerce dataset through empirical analysis by tuning various hyperparameters to enhance prediction efficiency. A computational time limit of ten minutes was used to ensure practicality. Using regression and classification frameworks, this approach involves predicting sales, profit, and delivery times, and identifying potential customers. Consequently, analyzing the behavior of the learning rate and its impact on the performance metrics indicated that increasing the learning rate can lead to improved model performance.

In today’s dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior’s originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively.

The capacitor based load method possess the capability to perform during non-uniform environmental conditions with auto-sweep functionality in the process

One of the key reasons contributing to a reduction in the performance of a solar photovoltaic (SPV) system is the presence of partial shade on the solar panels. It is necessary to use maximum power point tracking (MPPT) in SPV systems to get around the non-linear behaviour of photovoltaic panels. This research analyses the reduction in power output of SPV systems caused by partial shading. In the latter part of the paper, the improved dynamic behaviour-based ant colony optimization MPPT approach under the effect of module-level partial shading is implemented in MATLAB®/Simulink®. Various partial shading levels were applied to SPV modules to evaluate the system performance. The proposed system follows the maximum power point with 99.9% accuracy and a response time of 2 ms. The addition of a weight coefficient accelerates the convergence of the algorithm.