AI based systems as a solution to more sustainable energy supply

Human civilization heavily relies on the existence of

energy

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.

For instance

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, it powers

airplanes

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aeroplanes

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to transport people and goods thousands of miles away in less than a day, allowing us to trade goods and even

to

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apply

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rekindle with our loved ones.

However

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, escalating

energy

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consumption has resulted in climate change issues that could impact humanity and threaten future generations. Several preventive and corrective actions have been taken,

such

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as the use of Artificial Intelligence technology, or AI in short, to preserve

energy

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supply and manage its consumption.

This

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essay will explore what AI is and the example of its global

implementation

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in the context of

energy

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supply conservation.

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Data

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The data

A data

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center

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is a facility with computing infrastructure, storage, and backup power (Hogan, 2022). It is designed to ensure the continuous operation of computer systems and

the

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apply

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stored

data

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. In 2022,

data

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center

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centers

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contributed 1-1.3% of global electricity use, which was estimated around 240-340 TWh (Rozite et al., 2023).

This

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figure is predicted to keep rising, until 1.86% in 2030 (Koot & Wijnhoven, 2021). Google, as one of

technology

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the technology

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giants in the 21st century which started as a search engine, has evolved their business with diverse products and services. The growing line of business has led them to expand their

data

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center

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facilities. As of 2023, they have been owning 24

data

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centers

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spread across the world

while

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innovating to make significant improvements towards their efficiency. The

company

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started implementing a neural network in 2014, trained to mimic cognitive

behavior

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behaviour

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via interactions between artificial neurons. The neural network would search for patterns and interactions between features to automatically generate a best-fit model (Gao, 2014).

Until in

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2016, the

company

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introduced DeepMind AI which aimed to improve the previous system’s utility. As per

report

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a report

the report

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by Google, the

company

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has managed to reduce the amount of

energy

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used for cooling by up to 40

percent

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per cent

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after the

implementation

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of machine learning. The

company

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collects historical

data

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from thousands of sensors within the

data

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centers

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centres

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and uses it to train an ensemble of deep neural networks (Gao, 2014). It explains that the research

of

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apply

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comparing predicted vs actual PUE has a mean absolute of 0.004 and

standard

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a standard

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deviation of 0.005. The mean absolute measures the average absolute differences between observed and predicted values in a dataset, where the closest

a

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score is to 0, indicates higher accuracy of the prediction model (Jierula et al., 2021). Meanwhile, the standard deviation indicates how dispersed the values in a

data

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set are from the average value, where the closest

a

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score is

to

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apply

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0,

suggests

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suggesting

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that the

data

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points are less

variative

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variation

and close to the average (Barde & Barde, 2012). Based on the

data

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, it is proven that the

implementation

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of machine learning in Google

data

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center

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centre

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is

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not only

benefit

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benefits

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the environment

,

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but

also

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have

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has

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a high accuracy in predicting

the

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apply

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usage trends. In conclusion, the

implementation

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of AI-based systems in various sectors has proven to successfully revolutionize

energy

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preservation and contribute to a more sustainable

energy

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supply. Extensive training

of

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in

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the

data

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set will be needed to produce a more humane model, and it will not occur in a short period. We can certainly continue harnessing the power of AI,

while

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hand in hand focusing on the goal of reducing carbon emissions.