How AI, Big Data and Machine Learning Are Shaping the Energy Industry

By Guest Contributor on August 1, 2019

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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Trending application of machine learning 2019

By Guest Contributor on May 21, 2019

Did you know? All coming activities will be done, with the help of AI either other applications. Machine learning technology doesn’t need to be introduced here; it has made easy the things in daily life. Machine learning is using Artificial Intelligence, making the system to predict the automatically results without the intervention of human beings.

Machine Learning Algorithms

Supervised Machine Learning algorithm, they are using both input and output and then the algorithm is used in order to predict the output result. In unsupervised machine learning, only input variable is available instead of an output variable. In Unsupervised Machine learning, data is divided into classes so, get more significant results.

Whatever the actions we’re opt, by using smart technology that’s Machine Learning, we don’t even know what activities are under the shadow of Machine learning. Thus, Machine learning is used in a lot of sectors whether it is in private life or other industries. Thus, let’s focus once various sectors of the software of Machine learning.

Machine Learning technology can protect the companies which are handling finance, from fiscal fraud which might happen in the future. Apart from that, machine learning can help to predict the upcoming opportunities that could be executed for additional investments.

Cyber surveillance will help to protect those institutions that are more under the shadow of financial risk and is able to take action so that particular fraud could be stopped. Thus, it is needed to measure inside the doors of machine learning companies as soon as possible in order to protect the fund related problems.

On hearing the name of Assistants, the very first thing strikes our thoughts are that, candidates are those who help to guide and assist for a particular direction and here we are speaking about the Machine learning established personal assistants who on the basis of the preceding setting pick our upcoming actions through best devices.

Siri, Alexa, Google now are a few of the examples of Virtual Private Assistants that help in assisting information, you merely need to inquire through voice and you will find the result instantly in line with this search. Machine learning based virtual assistants such as Amazon’s Alexa, Google Assistant, and Apple’s Siri who are operating on our smart speakers and speakers are making our day to day life easier and entertaining.

Role of Alexa assistants individually

In Alexa, you have to decide on a routine up and whenever you state “Alexa, good morning,” the lights of your room will turn on automatically along with your favorite playlist would start playing itself because this Alexa, a digital assistant run on smart speakers.

Let’s understand how Siri, which is a smart device of this iPhone, may entertain your day to day life. Well, if you say “Siri, I am going home”. This will open the navigation management in addition to sends the text message into your family at the exact same time. Yes! We cannot deny that Machine Learning is the foundation of those private assistants as they get input and supplies the output in the type of the result according to your requirements.

Machine Learning Program in Marketing and Sales

Marketing and earnings on the basis of machine learning technologies are such a wonderful approach to keep the clients always in touch in order to buy your merchandise. Well, how can this be possible? Simple! With the support of Machine learning technology, you’d have the ability to examine the purchase background of the clients and would suggest those goods in the recommendations in order to generate the customers buy it for the next time.

So, it could be said that, it is told before that Machine learning technology forecasts future events on the basis of earlier participation, similarly in the case of advertising and sales, it is must say that, on the grounds of previous captured client’s likings, it boosts future sales and promotion.

Machine Learning Program in Predictions while traveling

Well, you know that, everybody travels with the help of GPS navigation. Machine learning technology here predicts the coming visitors on the way for that time being linked together with the GPS, your current place and velocities are being connected with central host of managing traffic.

How Machine learning impact on traveling?

You may have booked cab on the internet, and you have noticed that it automatically shows you the estimated cost of the ride. So this is all because of Machine learning. At times it also happens when you select the option of “sharing ride”, it automatically reduces the price of the ride. This is happening with the wisdom of machine learning technology.

Machine learning is playing a significant part in the medical industry too. Sensors that are fixed in the wearable of the individual so as to provide advice regarding the individual’s condition, heartbeat, blood pressure, etc.

The information that’s gathered through the detectors could assist the doctors in assessing the health and condition of an individual. Doctors can forecast the approaching health issues that may fret about the sufferers and if, if you’re running a healthcare department, do consult a good computer software development company in India which may assist you in various manners in order to maintain decent relation with your physician.

Machine Learning Application in Social Media Services

How entertaining and colorful your social media has become? No matter the item is wandering in mind, social media begins flashing the advertisements of that specific interest.

So, this is about that Social Media has connected with Machine learning so as to make your social presence beneficial and knowledgeable.

Let us see the impact of Machine learning on Facebook

Here is a really simple concept where Machine learning is dominating the popular program Facebook. How? Well, actually by indicating the various friend suggestions. On the basis of experience, Facebook keeps noticing the buddies you may connect and the profiles that you have ever visited.

Another manner where Machine learning is working on Facebook and that is when you upload an image with some friend of yours- Facebook immediately admits the exceptional feature of that individual after going through your friend list.

Final thoughts

This is how Machine learning is always making your lives simpler and entertaining. In the above-given examples, you may have understood how Machine learning is helping to forecast your output in the shape of future activities.

As its well-known that machine learning is amazingly revolutionizing the world, there are various mobile app development firms in India which are providing the supply of building ML-based applications.

This article is contributed by Ashish Goyal, Digital Marketing Specialist at Xtreem Solution, a leading mobile app development company; you can hire app developer from our robust team.

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Is Python a Good Language for Machine Learning?

By Guest Contributor on January 26, 2019

Nowadays, Python which is a general-purpose high-level programming language has been increasingly used in machine learning algorithms. It is usually favored for applications varying from web development to scripting and process automation.

Willing to start a small business or already working in an MNC, Python provides a huge list of benefits for all. The usage of Python cannot be limited to only one activity as its growing popularity has allowed it to enter some of the most popular and complex processes like Artificial Intelligence, Machine Learning, Data Science, Natural Language Processing and much more.

Machine learning is the technology to parse data by using algorithms and also make decisions based on it. It is a field of Artificial intelligence that uses statistical techniques for the ability to learn from data given by computer systems without being explicitly programmed.

What is Python?

Python is a high level, interpreted and an OOPs based interpreted programming language. It is considered as robust and highly useful being focused on rapid application development(RAD). It works well by connecting existing components together and because of easy leaning, it has become one of the fastest growing languages in terms of scalability and adaptability. Also, the broad support and constant evolving libraries make Python a better choice for any project be it WebApp, Mobile App, IoT, ML, Data Science or AI.

Is Python a good for Machine Learning?

The answer is definitely yes. What makes Python stand out is its exceptional qualities which undoubtedly allows developers to opt it in machine learning.

#1. Expansive selection of libraries and frameworks :- Python accords for a variety of specific libraries used for scientific computation and data analysis. These libraries have been upgraded continuously to reduce the constraints that developers faced before. Here are a few important ones from many libraries to perform data analysis.

#2. Matplotlib :- A 2D plotting library which offers data visualizations in the form of histograms, power spectra, bar charts, and scatter plots.

#3. NumPy :- To perform scientific computing by assorting high-level mathematical functions to operate on multi-dimensional arrays and matrices.

#4. Pandas :- Delivers data structures and operations to change numerical tables and time series which is developed on top of NumPy.

#5. SciPy :- Offers effective routines for numerical integration and up-gradation in association with NumPy arrays.

#6. Easy to Learn :- Python is considered as the popular data analysis tool which is ahead of SQL and SAS. The developers are rushing towards the Python for easy to learn and code by promoting an easy-to-understand syntax when compared other data science languages like R and leads to a shorter learning curve. To help data scientists and engineers work in a collaborative manner, a tool names Jupyter is used for writing code and text within a web page’s context.

#7. Scalable :- Python is best known for its simple, concise and readable coding properties. As machine learning relies on complex algorithms developers have to worry less about convolution of the coding. Python is faster to use than Matlab and Stata by emerging as a scalable language. It is also utilized by YouTube for flexible problem-solving situations.

#8. Less Code :- Machine Learning involves a lot of algorithms which provides ease of testing with the help of Python. It also helps in easy writing and execution of codes to implement the same logic with the same logic as other OOPs languages. Python’s interpretation approach enables to check code methodology.

#9. Platform-Oriented :- Python comes up with the flexibility to provide an API from its existing language which itself provides extreme flexibility and becomes platform independent. You can get your application running in a new OS or other platforms just by making a few changes in codes. This approach helps to save the developers time in testing on different platforms and gets rid of code migration.

#10. Boatload of support :- As python is an open source programming language, it has a wide number of developers who are ready out there to support and guide through it. Python comes with a huge community of developers with a host of resources which are available to speed up their tasks. There is a huge community of active coders who are willing to help programmers from their early stage of developing cycle.

Wrap Up

We can conclude Python as an easy, simple, powerful, and innovative language because of its broader usage in a variety of contexts which are not associated with data science. Understanding Python and using it as per its respective strengths can refine you as a machine learning programmer which should be versatile and stay at the top of every problem. In the end, it is concluded from the features that nothing suits exceptionally well to handle the complexity of machine learning as the Python does. The decision-making ability derived from machine learning and reduced complexity of Python makes a perfect match in heaven.

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