Energy Monitoring for Renewable Energy System Using Machine Learning Algorithms

Background Consumption of electricity always varies based on demand. The load cluster pattern aims at categorizing periodical changes over a specific time. Predicting the electric load was the initial goal of this study. Additionally, the outcomes of the load prediction were utilized as data for categorizing electrical loads using a descriptive-analytical method.Objective The study has dealt with a matching of load-side electric demand with the electric supply. To ensure dependable power-generating stability, it is vital to anticipate and categorize loads. Thus, the research presented here has focused on electrical load forecasting and classification.Methods Alternative algorithms, including Naive Bayes, decision tree, and support vector machine classifier, were employed to address the cluster pattern. The data used for this research presentation was collected from the D Block of the Kamaraj College of Engineering and Technology, K. Vellakulam, India, every 15 minutes. Multiple unsuitable loaded circumstances were ignored during the pre-processing of the dataset. Additionally, other algorithms, like Naive Bayes, decision tree, and support vector machine, were used to categorize the raw data. The processing of data was done by a feature selection approach.Results The performance was predicted by comparing the entire machine learning algorithms. Out of the machine learning techniques, an accuracy of 4.2% for Academic Block 4, a precision of 33% for Boys Hostel, a recall score of 4.7% for Academic Block 4, and an F1 score of 5.3% for Academic Block 4, were obtained.Conclusion In the study, the decision tree algorithm has shown promising performance than the other machine learning techniques used.