Global energy transition revolution and the connotation and pathway of the green and intelligent energy system

The essence of energy system transition is the"energy revolution':The development of the"resource-dominated"energy system with fossil energy as the mainstay has promoted human progress,but it has also triggered energy crisis and ecological environment crisis,which is not compatible with the new demands of the new round of scientific and technological revolution,industrial transformation,and sustainable human development.It is in urgent need to research and develop a new-type energy system in the context of carbon neutrality.In the framework of"technique-dominated"new green and intelligent energy system with"three new"of new energy,new power and new energy storage as the mainstay,the"super energy basin"concepts with the Ordos Basin,Nw China as a representative will reshape the concept and model of future energy exploration and development.In view of the"six inequalities"in global energy and the resource conditions of"abundant coal,insufficient oil and gas and infinite new energy"in China,it is suggested to deeply boost"China energy revolution',sticking to the six principles of independent energy production,green energy supply,secure energy reserve,efficient energy consumption,intelligent energy management,economical energy cost;enhance"energy scientific and technological innovation"by implementing technique-dominated"four major science and technology innovation projects',namely,clean coal project,oil production stabilization and gas production increasing project,new energy acceleration project,and green-intelligent energy project;implement"energy transition"by accelerating the green-dominated"four-modernization development',namely,fossil energy cleaning,large-scale new energy,coordinated centralized energy distribution,intelligent multi-energy management,so as to promote the exchange of two 80%s"in China's energy structure and construct the new green and intelligent energy system.

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.

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.

On the connotation, challenge and significance of China’s “energy independence” strategy

The world’s energy is in the "third major transformation period" from fossil energy to new energy, and all countries in the world have formulated energy development strategies. Through advanced deployment, the United States is about to achieve "energy independence" relying on "unconventional oil and gas revolution". China’s energy development is faced with four challenges:(1) The population base and economic development scale determine the "totally huge amount" of energy consumption;(2) the "coal rich but oil and gas insufficient" resource structure determines the "unclean" energy consuming structure;(3) the increasing dependence on imported oil and gas determines the "unsafe" energy supply;and(4) the unconventional oil and gas endowment makes it impossible to achieve energy independence by copying the American model. From the perspective of the world energy trend and the unique situation of China’s energy, we put forward a "three-step" strategy for China to achieve "energy independence": From 2020 to 2035, "energy supply security" will be addressed by "cleaning coal, stabilizing oil and gas production and vigorously developing new alternative energy";from 2035 to 2050, the vision of "production independence" will be realized by relying on "domestic production and overseas oil and gas mining rights";from 2050 to 2100, "intelligent energy and new energy" will help China realize "energy independence". The two important signs of China’s "energy independence" are that domestic production accounts for more than 90% of the domestic consumption and clean energy production accounts for more than 70%, and energy security realizes "independence and self-control" and "long-term security". The strategic significance of "energy independence" is to ensure national energy security, drive the development of relevant major industries, achieve energy management reform, and implement the environmental protection goal of zero carbon emissions. The "energy independence" of China is a strategic mission, it might be fulfilled in the future with the growth of the state’s power, even when the domestic energy production does not catch up with the domestic consumption. Perhaps the world’s new technological revolution will exceed expectations, and China’s "energy independence" dream will eventually come true.

Renewable and Nonrenewable Energy Flow Resiliency for Day-to-Day Production and Consumption

Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as electricity for daily needs from an uninterrupted source of energy no matter either renewable or nonrenewable.Causes of resilience issues include power surges,weather,natural disasters,or man-made accidents,and even equipment failure.The human operational error can also be an issue for grid-power supply to go down and should be factored into resilience planning.As the energy landscape undergoes a radical transformation,from a world of large,centralized coal plants to a decentralized energy world made up of small-scale gas-fired production and renewables,the stability of electricity supply will begin to affect energy pricing.Businesses must plan for this change.The challenges that the growth of renewables brings to the grid in terms of intermittency mean that transmission and distribution costs consume an increasing proportion of bills.With progress in the technology of AI(Artificial Intelligence)integration of such progressive technology in recent decades,we are improving our resiliency of energy flow,so we prevent any unexpected interruption of this flow.Ensuring your business is energy resilient helps insulate against price increases or fluctuations in supply,becoming critical to maintaining operations and reducing commercial risk.In the form short TM(Technical Memorandum),this paper covers this issue.

Energy Driven by Internet of Things Analytics and Artificial Intelligence

Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.

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.