Transforming Energy & Utilities with Big Data Analytics
It isn’t until the lights go out and the HVAC system stops working that most of us appreciate how important utilities are to our everyday lives. For the most part, energy and utility companies have operated in a predictable, linear way with reliable service, in spite of population growth and geographical expansion.
Today, major factors including aging infrastructures, political pressures, extreme climate factors and consumer behavior have energy and utility companies rapidly adopting new technologies that are transforming the way the industry operates.
The emerging technologies include smart meters and smart grids. These technologies are providing companies with new capabilities for forecasting demand, shaping customer usage patterns, preventing outages, optimizing unit commitment and more.
At the same time, these smart meters and smart grids are generating an unprecedented volume, speed and complexity of data. For example, going from one meter reading a month to smart meter readings every 15 minutes works out to 96 million reads per day for every million meters. The result is a 3,000-fold increase in data that must be managed. And, the flow of data from ever more sources is bound to increase as time goes on. This compounding increase in the volume, velocity and variety is referred to as big data.
Before we get into a big data discussion, let’s consider how energy and utility companies are becoming more data driven and how building a solid foundation to manage, analyze, and use this information will pay off for the utility companies, their customers and communities.
Data gathered from smart meters can provide better understanding of customer segmentation, behavior and how pricing influences usage—if companies have the capability to use that data. For example, time-of-use pricing encourages cost-savvy retail customers to run their washing machines, dryers and dishwashers at off-peak times. These customers not only save money but also require less generation capacity from their energy company, which means lower capital outlay for new generation and overall greater operational efficiency for utilities.
But the possibilities don’t end there. With the additional information available from smart meters and smart grids, it is possible to transform the network and dramatically improve the efficiency of electrical generation and scheduling. However, the new mix of intermittent generation resources available requires more granular forecasting, load planning and unit commitment analysis than ever before to avoid inefficient energy trading or dispatching too much generation.
Here’s where data management and analytics solutions become important. Using the meter and grid data as a base, the various types of analytics can be applied to better understand a number of things, including:
- How pricing changes affect changes in demand.
- Which customer segments are most likely to respond to requests to reduce power.
- Detecting when the power flowing thorough a substation doesn’t match the input. from the meters, which is a likely indicator of energy theft or diversion.
- Understanding which portions of the distribution system are being stressed beyond their design points and should be candidates for maintenance activities.
- Determining where new generation investments should be made.
By now, you get the idea of how powerful data can be when it is strategically managed, analyzed and used to transform operations, plan infrastructure, or shape consumer usage patterns.
Across many industries, including energy and utilities, data is being viewed as the new renewable resource. The amount of data in the universe will never abate, and hence the concept of big data.
If you would like to learn more about how utilities are meeting the challenge posed by the growing volume, velocity and variety of information in the energy industry, watch this short introductory video, "Big Data, Big Opportunities: Energy & Utilities."
Read how Vestas Wind Systems is pinpointing the optimal location for wind turbines to maximize power generation and reduce energy costs
Or watch the video:
IBM hosted a Twitterchat with Ron Melton, Director of the Pacific Northwest National Laboratory (PNNL) Smart Grid Demonstration Project. Big data evangelist James Kobielus wrote this blog recapping that conversation, "Big Data: What Drives You and Where Do You Start."
For information on products mentioned in these case studies, visit the IBM big data platform site.