Solar Power Prediction Using Machine Learning

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.Comment: 7 page

Clinical and Genomic Evolution of Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infections over Two Time Periods at a Tertiary Care Hospital in South India: A Prospective Cohort Study

Abstract Introduction The objective of this study was to examine the evolution of carbapenem-resistant Klebsiella pneumoniae (CRKp) infections and their impact at a tertiary care hospital in South India. Methods A comparative analysis of clinical data from two prospective cohorts of patients with CRKp bacteremia (C1, 2014–2015; C2, 2021–2022) was carried out. Antimicrobial susceptibilities and whole genome sequencing (WGS) data of selected isolates were also analyzed. Results A total of 181 patients were enrolled in the study, 56 from C1 and 125 from C2. CRKp bacteremia shifted from critically ill patients with neutropenia to others (ICU stay: C1, 73%; C2, 54%; p = 0.02). The overall mortality rate was 50% and the introduction of ceftazidime-avibactam did not change mortality significantly (54% versus 48%; p = 0.49). Oxacillinases (OXA) 232 and 181 were the most common mechanisms of resistance. WGS showed the introduction of New Delhi metallo-β-lactamase-5 (NDM-5), higher genetic diversity, accessory genome content, and plasmid burden, as well as increased convergence of hypervirulence and carbapenem resistance in C2. Conclusions CRKp continues to pose a significant clinical threat, despite the introduction of new antibiotics. The study highlights the evolution of resistance and virulence in this pathogen and the impact on patient outcomes in South India, providing valuable information for clinicians and researchers