Inefficiencies in the electrical grid mean that massive amounts of energy are wasted each year. Technology, such as smart metering and big data can help eliminate this waste. We look at nine ways that utilities can get smart using big data.
The need for green energy is critical and big data can play its part in delivering it. Danish energy company Vestas Wind Systems has been using Big Data analysis for three years in order to define the optimum sites at which to locate wind turbines. The system analyses weather reports, tidal phases, geospatial and sensor data, satellite images, deforestation maps, and weather modeling research to pinpoint precisely where to place new turbines. The result? The analysis, which used to take weeks, can now be done in less than an hour, and the company produces close to 20 percent of the world’s wind energy.
Massachusetts Institute of Technology (MIT) has put together a big data-enabled utility management system that hints at the future of smart building management. The system constantly monitors events across the campus, delivering actionable insights on events: the system can identify energy leaks and predict future faults. This means MIT has a better grip on its utility costs. Analyst Navigant estimates the market for similar HVAC energy recovery systems in North America will grow to $1.6 billion by 2020 from about $775 million this year.
As sensors improve across utility networks so utility companies will gain access to increasing quantities of data for analysis to help them predict energy demand – 40 percent of US households now have smart meters. Big data systems enables them to combine network-based data with third party information, such as weather forecasts, travel information and any other information that could impact energy demand. On the basis of analysis of such data energy companies should be able to not only see what energy is in use now, but also to forecast how much energy may be required next week, or next month. EDF subsidiary, Edelia, uses similar big data analysis systems to predict energy demands across the next 12 months, updating these every 30-minutes – such is the power of big data.
When an energy utility suffers a problem in its distribution system it impacts customers and can mean wasted energy. Intelligent machines can tell you when they’re malfunctioning and smart monitoring across energy company infrastructure can do the same. Use these with big data analysis systems and energy firms won’t just be able to see when a problem takes place, but will also be able to determine if there’s any pattern to events that may suggest a bigger problem.
Utilities will receive huge quantities of information from across their sensor-laden networks. This information will give them insight into the patterns and conditions of use of their system. That’s great, but this same information can be combined with what they know about their equipment to identify future maintenance needs. This enables intelligent maintenance, minimizing faults and reducing downtime and disruption, delivering better service and better systems.
Smart meters aren’t just connected to utility networks, but also to billing systems. Combining information from both with big data analysis and utilities will quickly become able to identify usage patterns that suggest fraud may be taking place, for example when energy is being diverted or stolen. Ofgem believes around £500m in energy is stolen each year, so reducing the incidence of theft will greatly benefit company revenue.
Energy is a commodity. As a commodity it is bought and sold across international energy markets. What if you had better models to predict energy demand in order to predict when energy should be bought and sold? With big data analysis, energy firms have a better chance of making good decisions based on accurate real time data when trading on the energy markets, enabling then to protect themselves (and customers) from the devastating impact of poor decisions.
All these customer and usage insights mean energy companies will have a better picture than ever on how their products are used. This provides an opportunity to create new revenue streams, such as advantageous smart home tariffs for residential owners, or packages designed to promote use of spare capacity during periods of low demand. Smart meters also allow energy firms to make recommendations to customers designed to help them use energy more efficiently and reduce bill shock. In the UK Ovo Energy already works with energy data to make service recommendations to customers.
It is important to note that we’re only at the beginning of assessing big data and its impact on smart connected utility systems. Take maintenance – we’ve talked about how these systems will be able to identify usage patterns in order to figure out when a network component may need replacement or repair. The next step will see the electricity distribution system recognize it is developing a problem only to assess and book its own repair – predictive analytics has arrived.
Big data systems have implications on all kinds of business sectors – read our white paper explaining some of the ways businesses can take advantage of it.
Jon Evans is a highly experienced technology journalist and editor. He has been writing for a living since 1994. These days you might read his daily regular Computerworld AppleHolic and opinion columns. Jon is also technology editor for men's interest magazine, Calibre Quarterly, and news editor for MacFormat magazine, which is the biggest UK Mac title. He's really interested in the impact of technology on the creative spark at the heart of the human experience. In 2010 he won an American Society of Business Publication Editors (Azbee) Award for his work at Computerworld.