Performance and Analysis of Soft Computing Techniques with Energy Management Framework in IoT Networks

Abstract

An EV (ELECTRIC VEHICLE) charging system based on machine learning (ML) has the capacity to generate precise future judgements based on previous data. A number of ML algorithms, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN), are contrasted in terms of their performances in optimisation. The outcomes verify the reliability of the use of KNN for the management of EVs to ensure high accuracy. The KNN model successfully minimizes power losses and voltage fluctuations and achieves peak shaving by flattening the load curve. Novel Sequence Learning-Based Energy Forecasting framework includes a unique mechanism for predicting future energy consumption. It uses sequence learning techniques, which are often employed in machine learning and artificial intelligence for tasks involving time series data. The goal is to forecast energy consumption efficiently and with low error rates. The cloud server and smart grids work together to manage energy demand and response effectively. These techniques used to clean, transform, and prepare the data for analysis. The framework incorporates energy decision-making algorithm specifically designed for an efficient forecasting. Short-term forecasting is essential for managing energy demand and response in real-time. It appears that this framework combines various technologies and methodologies to create a comprehensive system for real-time energy management in an IoT environment. The focus is on efficient and accurate energy forecasting and decision-making to optimize energy consumption.