Predictive analytics energy

Predictive Analytics in the Energy Sector

Predictive Analytics in the Energy Sector

With recent developments in social media, customer satisfaction has become even more important than ever. A single positive tweet from your customers can have more impact than thousands of dollars (and sometimes even more) spent on marketing activities.

Unfortunately, the energy sector has lagged behind in providing good service to its customers. In a 2019 research conducted on 4,000 people regarding customer service, energy providers ranked lowest on the charts. 

But the good news is that managers began to invest more time and effort to improve this negative trend with the help of predictive analytics in the Energy Sector.  

Quickly resolving customer-related problems will always have positive effects on your business, but ensuring that these problems rarely occur is exactly what differentiates great service from good service.

Predictive analytics is exactly the tool that helps energy companies be proactive in the face of problems such as power outages, outdated equipment, unusual spikes in energy use, etc.

Before exploring how exactly all of this can be accomplished, let’s quickly review predictive analytics and what it actually means.

What is predictive analytics?

Predictive analytics is a means of identifying the probability of future events based on historical data and analytics techniques like statistical algorithms and machine learning. 

This means that any business can use its historical data and predict future trends and customer behaviors. This can be beneficial for various fields such as marketing, finance, and even law enforcement. For example, if a financial institution has determined that there is a high probability that an individual will default on the repayment of borrowed funds, then they can choose to deny or limit additional borrowing. 

Predictive analytics also provides marketing professionals with the ability to predict customer behavior and preferences. By first understanding customers based on their demographics, psychographic, and behavioral data, companies can better cater to the needs and wants of their customers.

Or let’s take the example of an eCommerce store. Let’s assume they introduced a new product and want to offer a 20% discount to a limited number of users.

By analyzing the historical data of its customers with the help of predictive analytics, the store can identify those clients who are most likely to buy the new product and therefore send the campaign offer only to them.

In this way, the store maximizes the probability of success of the campaign.

Now that we understand what predictive analytics is, let’s see how it is used for the energy sector and what are its benefits.

Predictive analytics for the energy industry

One of the biggest causes of dissatisfaction for the energy company’s customers can be the power outage

This is especially true today since almost all the activities we do throughout the day (work, study or free time) somehow include electronics that depend on the power source. 

Things like weather conditions (strong wind, lightning, snow, rain, etc.), vehicle accidents, or equipment failure are the main reasons for the power outage.

Being able to predict the likelihood of any of the above-mentioned events and having a proactive approach to it is what energy companies can do to avoid outages, and predictive analytics is exactly the tool that can help achieve this.

How can the energy sector benefit from predictive analytics?

benefits predictive analytics

Before implementing predictive analytics tools, you need to understand whether the data you have is satisfactory.

Knowing how accurate the data is will be one of the most important parts. Is your company capturing the data it wants predictions on? Or is the amount of data sufficient for predictive analytics tools to deliver quality results?

After answering these questions, the next step is to train the models based on historical data to learn and be able to predict certain events and trends.

For example, by training and learning about degree-day data (climate heating and cooling), the model can give the probability of an upcoming winter storm that can damage your electrical grids and lead to a power outage.

With the help of this information, you can prepare in advance and prevent the problem from occurring or, if it still occurs, quickly send out the team and fix the problem.

Imagine the time that would be wasted waiting for a customer to report the issue, then finding out the exact location of the issue, and then telling your team members about it and dispatching them to fix it.

These days are long gone thanks to innovative solutions like predictive analytics. 


From equipment failures to information on customer behavior, predictive analytics is the tool based on which electricity companies are obtaining a competitive advantage in the market, however, the implementation of this solution requires a team of professionals with years of experience in similar solutions.

MaxinAI is happy to offer you a free consultation, go over your project and show you the value we can bring to your business. 

As the demand for energy grows ever higher, let us help you ensure smooth and uninterrupted service to your customers with innovative machine learning solutions.

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© 2021 - MaxinAI | All Rights Reserved
© 2021 - MaxinAI | All Rights Reserved