Linear regression approach for performance evaluation of ES with load impedance variations of non-critical and critical load

Intelligent system for the optimization of eco-mobility operation processes aimed at public transportation in the state of Jalisco

“…To address this limitation of LLR models, this study suggests the application of heteroscedastic LLR and data counting methods for VMT estimation. Since as suggested by (Rokde & Thosar, 2023) these methods perform better than LLR models in terms of data fitting and prediction accuracy.…”

“…The investigation of how to treat the data in this area that was relevant and significant began and found the work of (Rokde & Thosar, 2023) where he details how renewable energy sources (RES) are being developed moderately as new generation additions to the modern electrical system. These developments perturb the stability of voltage and frequency in the distribution power lines, for this the occurrence and perturbation data were analyzed by adding a spring device or electric spring which for the research studies the data of the variation of the load impedance generated by the RES to the electrical network by analyzing the performance of the electric spring through MATLAB simulations, The loads considered are linear.…”

“…Analyzing the above, this article was of relevance to form the beginning of a firm analysis oriented in data work, highlighting linear regression, since in the work of (Rokde & Thosar, 2023), it was possible to determine the relationship between the independent and dependent variables. Linear regression equations were constructed for P is, Q is and V is using the aforementioned independent variables, which allowed studying the behavior of energy storage (ES) under under and over voltage conditions.…”

“…Linear regression was also used to perform correlation analysis based on the R^2 value between the independent and dependent variables under under and over voltage conditions. For the purpose of this work, the linear regression used in (Rokde & Thosar, 2023) was fundamental to model and understand the relationship between the variables involved in the study of energy storage behavior under different voltage conditions.…”

“…With the orientation of the previously detailed work, it is proposed to integrate in the first instance, simple regression techniques to treat data such as the KM traveled at the beginning of each bus load, the initial load percentage, and the time it takes to load from the current percentage to the end (100%), aided by the database generated by the digital tool, it is not proposed to use MATLAB as highlighted in (Rokde & Thosar, 2023), but rather to support the work with the help of machine learning tools such as Machine Learning.…”