10 New Ways Energy and Utility Providers Will Use Predictive Data in 2022

Find out how effective communications through data storytelling will be key.

Communication is set to play an increasingly vital role in helping utility providers navigate an increasingly challenging market environment. Energy production alone won’t be enough to differentiate energy providers. Utility companies are going to have to show how emerging technologies give consumers better, more reliable service at lower costs.

As data science continues to develop, new predictive modeling capabilities offer valuable solutions for energy and utility leaders who need to reduce operational expenses and improve efficiency. These organizations are currently undergoing a transformation in predictive modeling and data visualization in ten key areas.

1. Predictive Maintenance

Combining machine learning capabilities with an industrial Internet of Things (IoT) framework allows hardware to automatically predict its own maintenance needs. This development relies on sensors that collect time-stamped operational data from individual components. When ML-powered predictive engines identify signs of failure, they can trigger maintenance operations autonomously.

This data can show consumers how reliable energy production systems are and demonstrate the value of proactive maintenance compared to expensive repair interventions.

2. Adapting Energy Production to Fluctuating Demand

Consumption forecasting is one of the most challenging aspects of running an energy company. New consumer behaviors and novel regulations will make it even more difficult for providers to optimize investment in energy production. Artificial intelligence can help predict patterns in consumption data that inform energy production standards in response to fluctuating demand.

This data gives energy providers the ability to manage their production more efficiently and pass on savings to consumers. Providers who communicate this to communities will enjoy greater market penetration than those who do not.

3. Reducing Operational Costs with Drone-Captured Imagery & Data Visualization

Drone-mounted camera equipment can easily capture a lot of information that human beings cannot. For example, thermal sensors can detect leaks and other anomalies from a great distance and even visualize events happening underground. This vastly reduces the cost of identifying maintenance costs compared to sending human employees in the field.

Images speak louder than words. Energy providers who use high-quality imagery to showcase their commitment to preventative maintenance will earn the trust of the communities they serve.

4. Improving Customer Experience

Customers are becoming more sophisticated in their energy purchasing decisions, and the demand for high-quality services is increasing. Predictive data modeling helps utility providers analyze customers and serve them according to demographics, behavior, and sentiment. This can lead to better outcomes when energy providers recommend products to customers, especially when paired with intuitive data visualization tools.

Consumers will appreciate the insight that data visualization offers. They can identify wasteful activities and reduce their spending significantly while lightening the overall load for the grid.

5. Assigning Customer Risk Zones Based on Bill Payment Behavior

Unpaid balances remain a problem for many operators in the energy sector. Providers typically rely on historical data to determine which customers are most likely to pay their balances and which ones may never pay. Predictive analysis and data visualization tools can help energy companies direct their efforts towards their best customers and identify problem accounts early on.

Energy providers that serve low-risk consumer groups will have a much easier time obtaining public and private investment. Use this data to communicate customer resilience to stakeholders in a highly visual way.

6. Enrolling Vulnerable Customers Into Government-Sponsored Energy Assistance Programs

Government assistance programs like LIHEAP can help low-income customers pay energy bills that may otherwise go unpaid. Customers who participate in these programs retain buying power while utility providers earn guaranteed payment for their services. However, many low-income customers aren’t aware that they qualify for hardship assistance. Predictive modeling can help utility providers identify these customers and introduce them to helpful programs before the last minute.

Energy provider data will prove vital for determining the success of government-sponsored energy assistance programs. Collecting, analyzing, and sharing this data will help policymakers create better programs in the future.

7. Developing IoT-Based Smart Grids

Energy providers who build infrastructure networks based on IoT technology can automate many different aspects of their operations. Customers who use smart home technology can integrate with these solutions to participate in highly efficient smart grids. This enables energy providers to improve demand management, protect sensitive equipment, and ensure disaster preparedness.

Energy providers who successfully navigate disaster scenarios earn an invaluable boost to their reputation. Demonstrating your resilience to unexpected problems helps consumers and policymakers understand the value of the work you do.

8. Forecasting Fluctuations in Consumption and Production

Many new energy infrastructure projects focus on renewable energy production with highly volatile resources like solar and wind power. With these kinds of energy sources, the ability to predict production fluctuations is critical for ensuring consistent performance during uncertain periods. This reduces the cost of imbalance adjustments and ensures greater profitability for renewable energy providers.

Accurate forecasting helps drive down the cost of producing energy, raising margins and improving consumer access. Show these projections to consumers so they can plan accordingly.

9. Failure Probability Modeling

Failure probability modeling is already a critical technology in the energy industry. Machine learning algorithms significantly improve the accuracy and efficiency of failure probability modeling for sophisticated energy production lines. This reduces unexpected operations failures, improving the provider’s cost efficiency, reliability, and brand reputation.

Utility failures are uniquely damaging to energy providers. Show consumers and stakeholders how accurate your failure probability models are, and show the steps you are taking to improve those models continuously.

10. Real-Time Demand Response Management

Providers are constantly searching for renewable energy sources and new ways to use energy efficiently. Successfully navigating the balance between demand and supply is key to reducing operating expenses associated with these tasks. Monitoring energy use metrics in real-time can help providers adjust energy flow according to the current demand rate, saving time and money while improving pricing accuracy.

Energy providers can use this data to build rapport with consumers. Show consumers how much money they save due to real-time demand response management and use that dialogue to position yourself as a trusted partner.

Capture, Analyze, and Visualize Data Better

Energy and utility companies that invest in predictive modeling and data visualization initiatives can significantly reduce operating costs while improving reliability and brand reputation. Now is the time to competitively develop the customer experience and offer energy users the services they demand.

The way energy providers communicate with customers is key to success in today’s environment. Hahn Agency is delivering on its mission to help energy companies capture, analyze, and visualize their data to create memorable and immersive customer experiences.