Smart Energy Management System Using Machine Learning

Abstract

Energy management is an inspiring domain in developing of renewable energy sources. However, the growth of decentralized energy production is revealing an increased complexity for power grid managers, inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization, minimize energy costs without affecting production, and minimize environmental effects. Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings, which necessitates energy optimization and increased user comfort. To address the issue of energy management, many researchers have developed various frameworks; while the objective of each framework was to sustain a balance between user comfort and energy consumption, this problem hasn't been fully solved because of how difficult it is to solve it. An inclusive and Intelligent Energy Management System (IEMS) aims to provide overall energy efficiency regarding increased power generation, increase flexibility, increase renewable generation systems, improve energy consumption, reduce carbon dioxide emissions, improve stability, and reduce energy costs. Machine Learning (ML) is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy (IoE) network. The IoE network is playing a vital role in the energy sector for collecting effective data and usage, resulting in smart resource management. In this research work, an IEMS is proposed for Smart Cities (SC) using the ML technique to better resolve the energy management problem. The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy, and 7.89% miss -rate.