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How is artificial intelligence energy more efficient?

How is artificial intelligence energy more efficient ?

Artificial intelligence Energy can reduce consumption and increase production.

artificial intelligence energy

Artificial intelligence energy efficience

The world is in a state of excessive energy consumption. 

Recently, various countries are making efforts to reduce carbon. 

The Democratic Party of the United States proposed the 'Fair Transitional Competition Act' to impose a carbon border adjustment tax. 

At the same time, the European Union (EU) also announced the 'Carbon Border Adjustment System' legislation along with the 'Fit for 55%' to reduce European greenhouse gas to 55% by 2030.

Korea announced that it would reduce greenhouse gas emissions by 40% by 2030 and achieve 'Net-zero' by 2050. 

Artificial intelligence (AI) can play an important role in this area. Recently, there is a movement to actually use Artificial intelligence energy.

What is Artificial intelligence?

Artificial intelligence (AI), as you know, is a branch of computer engineering technology designed to make computers behave like humans .

There are many ways to implement artificial intelligence, but machine learning (ML) is a method designed to recognize and judge related situations by teaching a computer to a specific field . 

The more the computer learns, the better the accuracy of the output relative to the input.

One of these machine learning methodologies is deep learning . In fact , it is a term with almost the same meaning as artificial neural network , but it can be seen that the rebranding was done due to poor recognition of artificial neural networks in the past.

Limitations of artificial neural networks

The multi- layer perceptron of artificial neural networks has fundamental limitations.

In the backpropagation learning process, basic learning is processed well, but the performance in inferring new facts is poor. 

In addition, there is a phenomenon in which data is missing due to the problem of the disappearance of the gradient, which makes learning difficult. For this reason, artificial neural networks can no longer develop and are on the path of decline.


A learning rule designed based on a neuron model that learns by receiving multiple input values, passing through an intermediate processing algorithm, and outputting results is called a perceptron. Detailed concepts are not covered here.

Error Back propagation

It refers to the process of correcting weights by sending values in reverse to reduce the error between the learning result and the actual value.

Artificial intelligence Energy: can reduce consumption?

The way Artificial Intelligence energy can be used in this sector can be divided into supply and demand.

On the demand side, the simplest way to save energy is to not use energy-based electronics. However, this method is difficult in reality.

Therefore, the key is to reduce energy consumption without causing inconvenience to users. 

It is important to reduce wasted energy use without affecting user convenience. The easiest example is when the lights are turned on in an empty space.

Artificial intelligence energy can be easily utilized in these areas; Artificial intelligence energy can induce energy savings by identifying and analysing the user's energy use status and showing it to the user. 

For reference, this is called User Feedback, and the Oxford doctoral dissertation announced that 5% to 10% savings were achieved through this.

Artificial Intelligence Energy: 'Urban Energy Management Service' in Glasgow

Artificial Intelligence Energy: 'Urban Energy Management Service' in Glasgow

Glasgow developed a city energy saving service. 

Alternatively, Artificial Intelligence energy can directly control the facility to reduce wasted energy. 

The city of Glasgow, UK, has developed an artificial intelligence energy called 'Urban Energy Management Service' to reduce energy use by its citizens, By creating a map in two-dimensional and three-dimensional form, it provides real-time energy use information and rates for each zone. 

In addition, it analyses power usage patterns based on usage and climate information and provides them for each zone, so that users can feel that the energy consumption is severe compared to other zones to induce savings.

The service has been applied not only to citizens but also to institutions, and has been applied to 29 schools. 

After six months of application, about 330,000 pounds (about 490 million won) was saved.

Artificial Intelligence Energy: 'Giga Energy Manager',Korea by KT


Artificial Intelligence Energy: 'Giga Energy Manager',Korea by KT

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Artificial intelligence Energy is also being developed in Korea. KT has developed an artificial intelligence energy 'Giga Energy Manager', which uses Artificial intelligence to streamline energy use in medium and large buildings. 

The service works in conjunction with the 'Building Automation System (BAS)' that controls building facilities, and directs building facilities to be controlled in the direction of energy efficiency with AI developed by KT. 

For reference, KT applied to three of its own buildings and achieved an average reduction of about 10%.

SK Telecom (SKT) launched 'E-Optimizer'. 

The service also uses Artificial intelligence energy to analyse energy use and improve efficiency. In addition, the Electronic Components Research Institute (KETI) announced that it had developed an Artificial intelligence energy  that controls heating and cooling. 

This technology is characterized by improving energy efficiency while operating heating and cooling according to the user's preferred indoor temperature.

Artificial Intelligence energy: ETRI Energy Management System (AdBEMS)

Artificial Intelligence energy: ETRI Energy Management System (AdBEMS)

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ETRI, a Korean public company, introduced the Artificial intelligence energy ‘Autonomous Distributed Energy Management System (AdBEMS)' for energy saving. 

This is an autonomous energy demand management technology created using artificial intelligence energy based on IoT data. 

It is a technology that optimizes heating and cooling, lighting, etc. by analysing energy consumption patterns for each area through machine learning. It can operate without people in the building. 

Artificial intelligence energy can analyse, predict, and manage how much energy each area will consume. 

For example, if zone A is usually analysed as using more energy than zone B, energy is driven to zone A. 

It is managed unmanned, providing convenience as well as saving energy. 

An official said, “It is effective for energy demand management services by district, building, and community.

The artificial intelligence energy 'Smart Home Energy Management Integrated Platform', a technology for home energy saving, was also introduced. 

It is a technology that measures and diagnoses household energy consumption in real time as a digital twin. 

Digital twin is a technology that predicts results in advance by creating twins of real objects on a computer and simulating situations that may occur in real life with a computer. 

Based on machine learning, it optimizes customized energy for each situation, time and generation. 

In particular, the goal is to reduce the energy consumption rate of home appliances by up to 10-15% in response to changes in the external environment. 

An ETRI official said, "Currently, we have secured a smart home with artificial intelligence energy management technology through it." 

ETRI also introduced customized factory energy management system (FEMS) technology that requires a lot of energy.

The technology is specialized in dissolution, food and pharmaceutical processing processes. 

Analyse the energy-consuming impact through artificial intelligence energy. Currently, the goal is to achieve energy savings of about 12-15% by industry. 

After verifying the technology in a large-scale process, it plans to commercialize it by applying it to small and medium-sized businesses.

Artificial Intelligence energy: 'Aquafish' made by Knotz

Artificial Intelligence energy: 'Aquafish' made by Knotz

(Shooting = Reporter Kim Mi-jung, Editing = Reporter Im Chae-rin)

Aquafish' made by Knotz a private Korean company Distributed an artificial intelligence energy  renewable energy system that uses wastewater discharged from sewage facilities, power plants, steel mills, and farms to generate electricity. 

It operates as an Artificial Intelligence energy semi-autonomous system based on 5G IoT.

The device receives data in real time through 5G. 

AI directly learns and analyses data. 

In case of emergency, the optimal alternative is derived through the learned data. All devices necessary for safety have been integrated. 

This is a system with optimal power generation by reducing installation costs, shortening time, and applying Artificial Intelligence energy semi-autonomous driving technology. 

It produces 10kWh to 50kWh of electricity and can replace the monthly electricity consumption of 100 households with about 29MWh of monthly electricity. 

A company official who participated in the exhibition booth emphasized, "It is possible to reduce about 153 tons of greenhouse gas per year through integration with the AI-based semi-autonomous system," and said, "It is perfect for carbon reduction and ESG management."

Artificial intelligence energy: increase production

Artificial intelligence energy can also be used to increase production. 

The first method is to streamline the facility management of energy sources.

East-West Power uses AI to develop a wind power generation diagnostic system.

According to East-West Power, if the diagnostic system is applied to all 82 wind power plants, it is expected to generate economic performance of 30.4 billion won over 20 years.

The second method is to maximize production efficiency when building energy production facilities. This method is mainly applicable to renewable energy power generation sources.

Denmark's Vestas, a company that develops an artificial intelligence energy wind power generators, has decided to cooperate with IBM, a global ICT company. The purpose of the collaboration is to design in a way that maximizes the energy production of wind power using Artificial intelligence energy . Here, IBM provides a system that analyzes the environment, such as weather, tidal differences, satellites, and forest level, and recommends a suitable location for a wind power plant for energy production.

HTS Solar provides a system for optimizing solar power generation. The system recommends a suitable location and size of sunlight based on geographic and weather information.

Artificial intelligence energy: increase production

The amount of solar power generation varies according to climate and geography

Of course, both supply and demand fields can be applied at the same time. Demand response (DR) is a prime example. 

The DR field is a technology that controls energy usage through price according to energy supply. 

For these technologies to be effective, it is necessary to analyze at what level price adjustments affect usage.