Machine learning has been impacting a vast variety of business sectors in the last couple of years, ranging from finance (with the Fintech boom) to eCommerce and many more.

Energy has also been another big part of what’s known today as the “machine learning revolution”, both from an architectural setup to the execution of risky tasks in the nuclear field, for example.

Machine learning and data science have been a considerably big focus in an industry which still amounts for billions of dollars every year, worldwide. Let’s analyse, therefore, what’s the impact of these technologies on this very sector.

Automation and The AoT

The so-called “Automation of Things” has become quite a big obsession in the warehousing business, after Alibaba made public that, by simply fully automating their whole supply chain in 2018, they were able to increase their productivity by over 70%.

Warehousing principles are applied to the energy sector as well, with dozens of Python-based tools being installed in control rooms to automatically keep control of the entire facility, its power and “metabolism”.

Automation in complex energy facilities such as the nuclear ones is a matter of security as well, given the fact that it decreases the chances of human errors which, in case, would cause major devastation.

The Evolution Of The Matter In The Next Couple Of Years

Machine learning is stated at over $2 billion in value and it’s definitely very likely to increase even more. Just by covering its applications in regards to what is going on for the architectural setup of machine learning and deep learning,

we can safely say that this niche part of the industry will grow both from a technological perspective and, most importantly, from a business one. Energy-related startups, following a Forbes study, have grown by at least 24% in 2018, leading to:

a). More job-related positions on the matter: the figure of the “automation engineer” has, in fact, grown visibly and tangibly in the past couple of years, peaking at over 15% of the entire engineer market, in terms of job positions and availability in terms of resources from companies.

b). More investment influxes in the next couple of years. With examples like Currant and Raptor, who went through millions of dollars in investments (already repaid), it’s safe to confirm how this subject will attract more tech moguls and investors in the nearest future.

How Does Big Data Work With Energy?

If there’s one thing which combines the energy sector and big data, that would definitely be marketing. All the biggest and most successful companies in the sector have been using data in order to properly target their potential customers.

From a marketing perspective, the energy sector basically wants to provide plans which are targeting every potential customer differently. With examples like the recent Cambridge Analytica scandal, although, this very matter has been highly questioned in the recent past.

Luckily, GDPR in Europe has stated some cardinal rules for what concerns data acquisition, especially in regards to big data and data points.

An Example Of An “Automated Power Plant”

It may sound like science fiction, but automated power plants which are working with heavy machine learning tools have become reality in the recent past. Dissecting their workflow, we were able to understand how and which algorithms are doing specific bits:

a). The main architecture is coded in either C++ or Java and represents the “muscles” of the automation process. By telling which specific parts are moving in a power plant, this saves a lot of time in communications and execution.

b). The “brain” of these tools is usually made by a combination of Python and R. Guidelines are given to the Python tool which then processes all the variables via generally R coded algorithms.

This is a very simplified version of an automated control centre in power plants, which escalates to tens of these algorithms in complex architectures such as nuclear power plants, for example.

Will This Be a “Problem” For Us Humans?

Technically, every single time a machine learning-related matter approaches the “mainstream” market, someone comes out saying that “this will be the bane of our jobs”.

Realistically speaking, though, the application of these tools to every single energy-related facility will most likely happen in 2060, therefore there is no reason to panic. Plus, surveillance will still be required to check if everything is properly into place and, most importantly, these tools must be guided and programmed, in the first place.

With this being said, there is absolutely no reason to panic. Power plants and every single energy-related architecture will still require some sort of human guidelines.

The Mobile World: Remote Control For Workers

In a recent study, it has been pointed out that some app developers in the UK have been working on mobile apps which were capable of controlling warehousing and management tasks straight from their smartphones.

In warehousing management, in fact, the usage of remote controls has become quite normal since 2015, therefore there are no reasons why this shouldn’t apply as well to power plant controls or anything else in between, especially if machine learning related, where variables and guidelines are interchangeable anytime.

Connection and Blockchain, An “Almost Tangible” Hypothesis

A big problem between power stations has always been the fact that communications could have been quite slow. Imagine if all the above-mentioned tools could be able to communicate between them instantly by relying on a simplified blockchain-based structure.

This could be quite game-changing and will, if properly set up, save a lot of errors, dangers whilst, at the same time, improving the station’s productivity. The evolution of this very matter will actively set the foundations in regards to the entire technological evolution of blockchain being applied to both warehousing communications and management.

An Overall Picture Of Energy, Machine Learning And AI In 2019

With everything that has been said above, it’s important to understand how the application of machine learning to the energy sector will set the future for other business and supplying sectors in the nearest future.

It’s no secret that robots will dominate the warehousing world in the nearest future and, therefore, it’s important to monitor everything from both a technological and business perspective.

At the minute, this very technology is being put into place and has reached the stability needed for it to become something which could be considered as “an industry-standard”.

To Conclude

Energy as a whole has always been a sector which has embraced new technologies since its very beginning. The “nuclear revolution” has been the first big implementation of new pieces of technology in this sector and machine learning/ML and everything in between will definitely be the second big industrial revolution in this very sector.

With all the examples mentioned above and after a proper analysis of how these tools operate, it’s definitely safe to say that automated features and big data will definitely become extremely big in this sector.

This is definitely an exciting time for every robotics student, data science and architectural technology engineer who’s looking for a career change.

This article is contributed by Alice Porter has experience as an Alexa App Developer and is now a freelance tech writer.

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