How Machine Learning, Big Data & AI are Changing Energy

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Artificial intelligence (AI), machine learning (ML), and big data are some of the most trending business keywords you hear these days. Most businesses incorporate AI and big data into their existing workflows and processes. Many are even finding practical ways to use AI to improve, optimize, and automate their core processes.

This affects businesses in many sectors, from healthcare to marketing, with one of the most important sectors being the energy and utility sector.

In fact, renewable energy companies (wind, solar, hydro, nuclear) have greatly benefited from the power of AI, machine learning  predictive models, and data science over the past years. They have managed to lower their costs, make better predictions, and increase their portfolio’s rate of return.

If your company operates in the energy sector – or consumes massive amounts of electricity – chances are that AI and data science can help to boost your business performance.

What are AI, ML & Big Data?

Artificial Intelligence (AI)

AI is probably the broadest term of the three. The term was coined as early as 1956, referring to any machine that can conduct human-like activities. This means any machine which can recreate a specific task that a human would usually perform.

For example, Google’s AI DeepMind learned how to play chess at a professional level using reinforcement learning in just a few hours. Another great example is natural language understanding, which is widely used in software like Alexa and Siri.

Artificial intelligence nowadays is also known as Narrow AI because it is not a completely autonomous thinking entity, but simply a machine which can conduct a specific task very effectively.

Machine Learning (ML)

This is by far the most “hyped” term of today. You hear it everywhere, especially whenever AI is mentioned. Despite the two terms being related, they are certainly not the same thing.

While AI refers to simulated intelligence in machines, ML is nothing but an interdisciplinary field which allows us to achieve some sort of artificial intelligence by using statistical techniques

In simple terms, machine learning algorithms allow a machine to train itself on a set of data so that it can learn how to perform a task more efficiently.

Big Data

Big data is the study and application of large data sets that are way too complex for traditional relational database management systems. Some of the most common actions performed on data sets are:

    • Capturing data
    • Storing data
    • Data analysis
    • Updating
    • Querying
    • Visualizing the data

Why are AI, ML and Big Data Trending now?

The terms AI, ML, and Big Data were coined several years ago. So you might be thinking, why have we heard so much about them in the last five years?

AI has been a buzzword several times over the past 60 years, and has gone through different stages of adoption. From being extremely popular to losing most of its funding in the 1970s (known as AI winter).

There are several reasons why AI is emerging once again, and at such a rapid scale:

  • Better computational power – Nvidia has been a cornerstone driver in the development of faster and cheaper Graphical Processing Units, which in turn are responsible for the rise of deep learning. GPUs now have thousands of cores and have greatly sped up deep learning algorithms.
  • New, advanced ML algorithms – Some of the biggest companies in the world like Google, Netflix, and Amazon continuously work toward improving how they train and apply their machine learning algorithms. They have made huge strides and have created machines and systems that can now carry out specific tasks more efficiently than human beings.
  • Big Data and HTAP systems – The emergence of big data and (HTAP or Hybrid Transaction / Analytics Processing) systems now allow for real-time insights and optimized decision-making capabilities.

Additionally, companies in every sector collect massive chunks of data on a daily basis, which make up great datasets for training machine learning algorithms.

How can Energy Businesses Benefit from AI, ML, and Big Data?

As we covered above, ML is a subset of AI. Big data, however, works in synergy with AI and ML to improve systems and business processes.

Due to recent and continuous improvements in technology, we can apply the following technologies to optimize the energy sector.

AI and Grid Management

One of the most interesting uses of AI in energy is grid management.

Electricity is delivered to customers through a complex network (also known as the power grid). The tricky thing about the power grid is that power generation and power demand must match at all times. Otherwise, issues like blackouts and system failures can arise.

Although there are numerous ways to store energy, the most common way is the ancient but still efficient method of pumped hydroelectric storage. It works by pumping water to a certain elevation and then harnessing it again by allowing it to fall onto turbines.

When dealing with renewable energy, it is difficult to predict the grid’s electricity production capacity. After all, it depends on several factors such as sunlight and wind.

Demand Response

When large swings in demand occur, it can be very expensive for countries which produce most of their energy through renewable energy sources. With most countries shifting towards green energy, responding effectively to swings in demand is becoming even more difficult.

Germany, for example, plans to cover 80% of its electricity consumption using renewable energy by 2050.

There are two main issues that countries like Germany will face. Firstly, swings in demand. It’s common for electricity demand to skyrocket on a specific day or period of the year (on Christmas for example). The second issue is weather volatility. If there is no wind, or the sky is cloudy, it can turn out difficult to keep up with electricity demand.

In both cases, supplemental stations or fossil fuel-powered facilities need to make up for the excess demand.

Solving Demand Response

To solve these issues, many countries are teaming up with companies to analyze and predict weather data, electricity demand, and so on.

Germany initiated a project with EWeLiNE, which aims at forecasting how much wind and solar energy to expect at a given time. This allows the country to make up for excess electricity demand by using non-renewable energy whenever necessary.

In order to accurately match supply and demand, they use large historical data sets to train their machine learning algorithms as well as data collected from the wind turbines or solar panels to effectively forecast weather and power changes.

Predictive Maintenance

Aside from helping to match energy production with energy consumption, AI is becoming a major driver in assuring the reliability and robustness of power grids.

In 2003, a massive blackout in Ohio was caused by a low-hanging high voltage power line brushing against an overgrown tree. The power system alarm failed and there was no indication that the incident had occurred. The electric company did not discover anything until three more power lines started failing for similar reasons. Ultimately, this oversight caused a cascade effect, resulting in the entire grid going down.

The blackout lasted for two days and affected 50 million people. Additionally, 11 people died and there were about $6 billion in losses incurred.

Where AI and Internet of Things (IoT) Step In

Machine learning techniques can be used to implement predictive maintenance (PdM). In essence, power lines, machinery, and stations are equipped with sensors that collect operational time series data (data accompanied by a timestamp).

From there, machine learning algorithms can predict whether a component can fail in X amount of time (or n-steps). Additionally, it can also predict the remaining useful life of machinery, or when the next failure may occur.

The main purpose of these algorithms is to efficiently predict machine failure, avoid blackouts or downtimes, and optimize maintenance activities and periodicity, thus cutting down on maintenance costs.

In the US for example, they started installing phasor measurement units (PMU) to prevent failures in the power lines. The PMUs track:

  • Voltage & Current
  • Location (through GPS)
  • Timestamp (in microseconds)
  • Device ID

Events like the Ohio blackout can now be averted completely. Ultimately, AI and ML can help energy companies switch from a reactive maintenance stance to a predictive maintenance stance.

Energy Source Exploration

Besides weather forecasting and renewable energy source optimization, AI and big data are being used in fossil fuel energy source exploration and drilling.

Two years ago, Exxon Mobil teamed up with MIT to produce self-learning submersible robots to explore the ocean surface. These robots will be equipped with machine learning algorithms to not only help them learn from their mistakes while conducting explorations, but also to carry out the same work that a scientist would, without the risk.

The robot will explore, record data about the ocean floor, and make an analysis based on the data. This will allow for exploring new locations to drill oil and natural gas along the ocean floor.

Save Costs while Lowering Energy Consumption

Switching to renewable energy sources isn’t just for governments and electric companies to focus on. In fact, numerous companies like Google and Microsoft have tried to make an impact on the environment and their bottom lines by lowering their overall energy consumption.

Google is well-known for its massive data centers established around the world. These data centers produce a great deal of heat, which require a massive amount of electricity to cool down.

To address the problem, DeepMind AI used machine learning algorithms to reduce energy cooling on its Google data centers by 40%. Aside from greatly reducing their utility bills, it also helped to lower overall emissions, reducing the carbon tax they would otherwise have to pay.

Next Steps for AI and Data Science in the Energy Sector

AI, machine learning, and big data certainly have a long way to go in the energy sector. With developed countries aiming for a completely green economy, maintaining a balanced, resilient, and reliable power grid is of top priority.

That’s where smart grids step in. Smart grids are power grids which combine the power of IoT, AI, and big data to create a digital power grid that enables a two-way communication between consumers and utility companies.

Smart grids are equipped with smart meters, sensors, and alerting devices which continuously gather and display data to consumers so they can improve their energy consumption behaviors. It can also be fed to machine learning algorithms to predict demand, improve performance, reduce costs, and prevent system failures.

Although smart grids are being adopted in several developed countries, we still have a long way to go before switching to 100% renewable energy sources, AI-controlled power distribution, and grid management.

Data Science Software for Energy

The future of renewable energy resources and smart grids looks bright. The emergence of machine learning, the ongoing optimization of AI, and big data analytics have vastly expanded what is possible.

However, there’s still much to be said for partnering with the right experts to ensure you get the most accurate, insightful, and actionable information out of your data. That’s where data science platforms like RapidMiner can come in. RapidMiner is a unified software platform that guides you from the early steps of data prep to deploying your predictive models.

We can help you to maximize efficiency to meet energy demands and better satisfy your customers. Download RapidMiner Studio, which offers all of the capabilities to support the full data science lifecycle.

Looking to drive real business impact with AI?

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Additional Reading

Kara Lanio

Kara Lanio

Digital Marketing Director at RapidMiner