Smart Energy Management System Using Machine Learning

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.

Potential and roadmap of CO_(2) emission reduction in urban buildings: Case study of Shenzhen

Buildings play an increasingly important role to determine the trend of CO_(2) emissions in cities.Whether CO_(2) emissions from buildings can be effectively mitigated has great significance for cities to achieve climate governance goals.The study takes Shenzhen,a China's megacity,as an example to examine how the penetration of newly emerging clean technologies and consumer-to-prosumer role transition of buildings will contribute to CO_(2) emission reductions.Based on a Low Emissions Analysis Platform(LEAP)model,the major results indicate that CO_(2) emissions of Shenzhen's building sector could be capped by 2022-2025 and substantially decreased by more than 60%by 2030.Acelerating energy efficiency retroftting of existing buildings and enforcing stricter design standards on new buildings could largely reduce CO_(2) emissions,but still unable to prevent them from growing.The intensification of building energy-saving management and promotion of distributed renewable energy use would bring additional potentials of emission reduction,enabling a peak-reaching and a rapid downward trend of building emissions.To achieve the potentials,close cooperation and synergic efforts between multiple stakeholders are advocated for establishing inteligent energysaving management systems,decarbonizing urban power supply,and popularizing distributed roftop photovoltaic power stations.