Abstract

The machine learning techniques into configuration validation and prediction systems, aiming to enhance performance and decision-making processes across various industries. The identified problems include the scarcity of solar radiation and wind speed data in certain locations, complexities in integrating real-time observational data into numerical models, and the susceptibility of complex machine learning models like LSTM. A machine learning-based optimization method is proposed, focusing on improving numerical weather prediction. Data on hardware and software parameters are collected to train a model for optimal parameter combinations in various environments. The Random Forest model aims to increase weather prediction accuracy, with hyperparameter fine-tuning to mitigate overfitting. The algorithm’s used are data handling, support vector machines (SVMs), K-Nearest Neighbors (KNN) classifier, and Random Forest classifier, aiming to gather relevant data, visualize patterns, and implement classification algorithms for improved system performance. It addresses data scarcity, enhances prediction accuracy, and mitigates overfitting. Integrating machine learning into validation and prediction systems offers efficient systems and informed decision-making capabilities, facilitating proactive maintenance and improving system reliability.