How Predictive Analytics are Redefining the Energy Sector

Share on facebook
Share on twitter
Share on linkedin
Share on email

Now more than ever, energy companies must rely on advanced analytics to satisfy customers’ expectations for more choices, greater transparency and reduced energy costs. And to remain competitive, utility and power generators must use these same analytics to accurately forecast future power consumption. After all, storing energy over long periods is costly and so is waste from over-production—yet a supply shortage can result in power outages during peak hours. So, how can energy companies better manage the balancing act between energy supply and energy demand, and do it as efficiently as possible?

The answer is predictive analytics.

From smart meters and automated fault switches to drone sensors and more, utility providers collect data every second of the day. With troves of data across a wide range of systems, devices, transactions and customer interactions, energy companies are the epitome of “big data.” But data collection at this scale is meaningless without the right tools and strategies to generate valuable insights. That’s why leading utility companies are turning to predictive analytics to uncover new sources of competitive advantage and improve business operations—and here’s how you can, too.

How are Utilities Taking Advantage of Data to Operate More Efficiently?

By fusing commercial and consumer sensor technology with business intelligence solutions, energy companies can glean new insights to streamline decision-making, support automated processes, and improve customer experience. There are endless ways to maximize the value of your data, but here are some of the most prominent use cases that our team runs into when working with energy and utility providers.

Predictive Maintenance

IoT and machine learning are at the heart of predictive maintenance (PdM). Everything from power lines and machinery to power stations and maintenance vehicles are equipped with sensors that collect time-stamped operational data. By coupling this sensor-driven data with custom software solutions, utility providers can help identify failing physical assets in real-time and even predict the remaining useful life of machinery; this also prompts utilities to take preventative steps that will help them avoid blackouts or downtimes, and optimize maintenance activities to reduce maintenance costs.

Adapting Energy Production to Fluctuating Demand

From short-term to long-term consumption forecasting, there are many ways to predict electricity demand. Historically, key variables included previous years’ consumption data, weather data, seasonality and the performance of a particular grid—but that’s changing. Since state and federal government policies often support offsetting costs to drive the transportation sector’s shift from oil to electricity, new consumers and electricity producers are emerging—and this is dramatically influencing demand forecasting. In fact, researchers predict that electric vehicles will compose 60 to 75 percent of total new light-duty vehicle sales in the U.S. by 2050, representing 13 to 15 percent of national electricity demand.  Renewable energy producers are popping up on every corner, from large companies to backyard solar and private wind farms—and there are no signs of them slowing down.

On-Demand Webinar: Reducing utility OpEx Costs with Drone Images, AI & Dashboards

With so many external factors to consider—in addition to operational and localized energy consumption—now’s the time for electric utilities to align production with coming demand.  But keep in mind: While getting demand forecasting “right” can pay enormous dividends, getting it “wrong” can lead to unnecessary capital and operational costs. For example, in 1974, the U.S. energy sector made plans to double power generation due to a forecast that projected 7 percent annual growth in demand, according to a Harvard Business Review study. It turns out, between 1975 and 1985, growth actually slowed to around 2 percent, causing project cancellations and a sharp increase in consumer prices.

Improving Customer Experience

Managing asset risks not only conserves costs and streamlines operations, but it also helps utility providers avoid unexpected outages by maintaining critical assets before failure strikes.  And nowadays, consumer standards are so high that failure isn’t an option; customers need to be notified about planned outages well in advance.

Predictive analytics and business intelligence can help electric utilities control and avoid asset failures, outages and penalties: Intelligence from IoT assets, smart grids and SCADA, along with customer data, can provide critical insights into a customer’s utility usage.  Armed with the right intelligence, utility providers can implement programs like “tailored rate plans” to provide discounts to subscribers who use less energy and incentivize heavier users to cut back. So, while predictive analytics give electric utilities insight into when issues might occur, business intelligence systems identify which user segments could be targeted for promotions or programs to manage consumption.  By delivering a personalized experience to customers, utilities can better manage supply and demand, improve customer loyalty and reduce customer churn.

Electric Utilities Benefit from Microsoft Power BI, Machine Learning and AI

There are as many business intelligence tools in the market today as there are electric utilities, co-ops and power producers. And while we work with a variety of tools, Microsoft is widely recognized for its security, cost-effectiveness and ease-of-integration. In fact, Microsoft was recognized in February of 2019 as a leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for the 12th consecutive year. We’ve even seen first-hand how Power BI—Microsoft’s self-service business analytics suite of tools—enables utilities to visualize mountains of data and share insights with other systems and team members, and even embed them in applications and customer-facing websites.

Our team has a long history of success helping large, complex organization not only harness big data, but also ensure compliance with regulatory requirements, privacy laws and security. To get started, we recommend Predictive Analytics Discovery—a methodology executed by our team of data scientists to analyze your data, statistically validate your data quality, and determine your machine learning readiness.

And remember: while predictive analytics is certainly driving competitive advantage for electric utilities, that’s just scratching the surface of energy sector innovation. Download our white paper, “Energy Sector Transformation with Advanced Analytics and IoT,” to learn how energy companies are adopting other emerging technologies to transform operations.

Stay Up-To-Date