Role of Data Science in the Energy Management Sector

In the energy industry, machine learning and AI still have a long way to go. Since developed countries want to have a fully green economy, it is very important to keep their power grids balanced, strong, and reliable. Smart grids come into play here. Smart grids are power grids that use IoT and AI to build a digital power grid that lets consumers and utility companies talk to each other.

Smart grids are made up of smart meters, sensors, and alerting devices that are always collecting data and showing it to customers so they can change how they use energy. It can also be used by machine learning methods to predict demand, improve performance, cut costs, and stop system failures. Even though smart grids are being used in a number of developed countries, we still have a long way to go before we can switch to 100% renewable energy sources, have AI handle power distribution and grid management, and manage the grid entirely with AI. There are so many ways that Data Science can be used to transform the energy sector positively. Here are a few of the most popular applications in the works today.

1. Demand Forecasting

Businesses in the energy field need to make sure their customers get the best service possible so that everything runs smoothly. Demand for call center support is always different, no matter what business it is in. But it's especially hard to predict in the energy business. When there are big changes in demand, it puts a lot of stress on call centers. One of RapidMiner's customers, FirstEnergy, gets 16 million calls for their 700 call center workers every year. They needed a streamlined forecasting system to understand all growth drivers and make accurate predictions.

FirstEnergy chose to use data science to predict call volume by looking at all factors that affect call volume, such as the weather forecast, past data, etc. In the past, they used an old method for forecasting, but it was very manual and based a lot on short-term correlation.

This new approach, which was based on data science, was fully automated and much more accurate (it had an average accuracy of 93% for the 90-day forecast), with detailed documentation and an understanding of where knowledge gaps were. Each call centre saved an average of $665k a year thanks to these forecasting tools.

2. Grid Management

Grid management is one of the most interesting ways in which data science is used in the energy field. Customers get electricity through a complicated network called the power grid. The tricky thing about the power grid is that the amount of power made must always match the amount of power used. If not, things like power outages and system breakdowns can happen.

Even though there are many ways to store energy, the most common way is pumped hydropower storage, which is old but still works well. It works by pumping water to a certain height and then letting it fall onto rotors, where it is used again. When working with renewable energy, it's hard to predict how much electricity the grid can make because it depends on many things, like the sun and the wind. With the help of data science, organizations can better predict and understand how to keep their power grids in balance.

3. Consumption of Energy

Switching to renewable energy sources is important for more than just states and power companies. In fact, a lot of companies, like Google and Microsoft, have tried to help the environment and improve their bottom lines by using less energy generally.

Google is known for the huge data centers it has set up all over the world. These data centers give off a lot of heat, which needs a lot of electricity to cool down. In order to solve the problem, DeepMind AI used machine learning techniques to cool Google's data centers with 40% less energy. Not only did it cut their utility costs by a lot, but it also helped cut down on overall emissions, which cut down on the carbon tax they would have had to pay otherwise.

4. Improving theft detection and smart grid security

With the need for energy growing, it's not surprising that some people and even companies may try to get electricity through illegal means. In recent years, the theft of energy has become a big problem for energy companies. Energy theft costs energy companies an average of $89.3 billion each year.

Data science is used by energy companies today to stop energy theft. Many companies have advanced metering infrastructures that tell how much energy is used. This lets them watch how the energy flows and spot any problems. By keeping an eye on how people use energy and matching it to cases of energy theft in the past, energy companies can find people who might try to steal from energy grids and take steps to stop them.

5. Planned Predictive Maintenance

AI is becoming a big part of making sure that power grids are reliable and strong, as well as helping to match energy creation with energy use.

In 2003, a big blackout happened in Ohio because a low-hanging high-voltage power line touched a tree that had grown too big. The power system warning did not work, and there was no sign that something had happened. The electric company didn't figure out what was going on until three more power lines broke for the same reason. In the end, this mistake led to a chain reaction that brought down the whole grid. The power went out for two days, and 50 million people were affected. In addition, 11 people died and about $6 billion worth of damage was done.

This is where Data Science and IoT (Internet of Things) come in.

Predictive repair can be done with the help of machine learning. Basically, sensors are put on power lines, machines, and stations to collect operating time series data, which is data with a time stamp. From there, machine learning systems can figure out if a part is likely to fail in X steps or time. It can also tell how much longer a piece of machinery will work or when it might break down again. The main goal of these algorithms is to accurately predict when a machine will break down, avoid blackouts or other downtimes, and optimize maintenance activities and schedules to reduce maintenance costs.

In the US, for example, they started putting in phasor measurement units to keep the power lines from going down. They can find out:

    Location (using GPS) of the voltage and current
    Date and time (in microseconds)
    ID of Device

A blackout like the one in Ohio can no longer happen. AI and ML can help energy companies switch from doing maintenance after something goes wrong to doing maintenance before something goes wrong.

6. Improving Customer Experience

Energy providers are still businesses at the end of the day, and they need their users to make money. Customers' needs and wants are the most important thing for every energy business. Energy companies can learn a lot about their customers based on how they act and how much energy they use. This information can be used to find useful connections between power supply and customer demand and to personalize services and recommendations for their customers.

7. Real-time customer billing

It's not strange that companies want to improve their customer service and make their customers happier. Energy and power companies are not falling behind. They work to make the service delivery, billing, and payment processes clearer, to improve quality, and to get rid of delays, misunderstandings, and things that can be argued about. Companies use a lot of different apps and tools to keep track of their many customers, billing, payments, and invoices. Customers, on the other hand, can also keep an eye on the deal.   The operational management software keeps track of operational activity and transactions in real-time and takes instant action on billing, payment, prepaid and postpaid services, and communication services.

8. Dynamic energy management

Dynamic systems for energy management are a new way to keep track of the load. This type of management takes into account all the traditional energy management ideas about consumer demand, distributed energy sources, and demand-side management. It also takes into account current energy problems like saving energy, reducing temporary load, and lowering demand. So, smart energy management systems have developed the ability to mix smart end-use devices, distributed energy resources, and advanced control and communication.

Big data analytics is a key part of this because it makes Smart Grids' dynamic control systems possible. This helps a lot to make the energy flows between sources and users as efficient as possible. In turn, the load forecasts and the use of renewable energy sources affect how well the energy management system works. Most of the time, smart energy end-use devices, smartly distributed energy sources, advanced control systems, and an integrated communication framework make up the dynamic energy management component. Dynamic energy management systems handle a lot of data that is collected through real-world solutions and methods. When big data analytics are used on this data, it helps to estimate performance and make smart suggestions for managing energy.

9. Demand response management

Smart energy management is at its most popular point because people are always looking for new ways to get energy and they need to use energy wisely. Getting the mix between supply and demand right is a key part of managing energy well. Both high and low demand rates cause a lot of problems and costs for both energy companies and customers.

So, demand response is a method that has been shown to work over time. Certain applications and solutions for real-time management let you keep an eye on how much energy is being used, choose which activity to focus on, and change the energy flow to match the current demand rate. Also, there are programmes that help people save money by getting them to use energy at certain times. So, customers get a chance to switch to a better price plan, and energy providers get a chance to reach the balance they want.

10. Getting better at predicting outages

Many companies have to deal with power outages all the time. Even though power blackouts have become less common over the years, they can still happen for many different reasons and leave thousands of people without power and stop businesses from running.

For example, bad weather caused a big power outage in the US state of Texas a few weeks ago that lasted for several days.

To deal with this, energy businesses are now using data science and other types of data analytics to find and predict outages better. With these options, energy companies can learn more about how weather affects power grids and where outages might happen. With this data, energy businesses can predict outages by figuring out the metrics and their threshold values. They can also find out why outages happen. Once the reasons have been found, energy companies can take steps to keep their energy flow in check and warn people about possible blackouts.

Conclusion: The energy and utility businesses are always compelled to provide high-quality services at an affordable price, without delays or problems, 24 hours a day, 7 days a week. In their daily lives and jobs, people need energy sources. Because technologies change and improve so quickly, the business world faces new chances and challenges every day.

Machine learning algorithms, analytic models, and big data solutions help businesses manage and make good use of their resources, control energy flows, regulate grids, optimize work, and avoid costly mistakes.  

Using real-time and predictive analytics, as well as data science solutions, takes a lot of money and a willingness to face challenges, learn, and start new, complex operations. But there are many good things about using data science in the energy and utilities field.