by Michael Madrazo, Detectent
As of May, 83 smart grid projects funded through the stimulus bill are underway in U.S. communities. The U.S. government has appropriated $4.5 billion of smart grid stimulus funds, as well as $7 billion of broadband deployment funds. These funds will ensure that the required communications network for smart grid reaches all urban and rural communities.
The smart grid evolution, which some might even call a revolution, is changing how utilities do business and interface with customers. We are transitioning from electromechanical meters with the same design of the late 1800s to digital smart meters. These next-generation meters contain sophisticated, two-way communications capabilities enabling an automatic meter infrastructure (AMI) that gives utilities more information than before. Having more information is one thing, but how it’s mined, monitored, modeled, managed and analyzed is another. More information is vital to better manage peak loads and to empower consumers to change their behaviors and consume less energy. With the prime driver to lessen carbon footprints and significantly reduce the need to build new power plants, utilities are starting to embrace a new way of thinking.
Power to the People
Energy presentation software products, which are quickly emerging, promise consumers the ability to better understand how much energy they’re using and make better choices. This premise is noble, but how many people will consult the software to check which lights are left on or see how much energy they’re using? Some monitoring tools require consumers to complete numerous questions about their energy needs. Few people will have all the required information for a system to perform consumption calculations. Many people might be willing to alter their behaviors, but changes must be easy, convenient and easily understood.
Boston Consulting Group research found that most consumers surveyed are willing to tap into smart meter information to conserve energy. Utilities to date, however, have not enabled people to use their two-way communication meters constructively. The survey shows that 66 percent of respondents said they would like more communication from utilities about smart meters and their value. Fewer than 30 percent of them recall getting information from utilities beyond their monthly bills.
Smart meters present an opportunity for utilities to gain closer customer relationships and learn about their energy needs and consumption. Who better than utilities to discuss and explain customer energy usage? By using time-proven, sophisticated algorithms and analytics, a utility can better understand consumers’ consumption and create ongoing, meaningful dialogue through regular communication and positive reinforcement. Whether for energy efficiency, demand response or pricing programs, utilities can analyze customers’ energy use and tailor programs specifically for customers based on their individual usage patterns, driving utility program success rates much higher.
Power to Utilities
Through AMI, utilities can show customers how much energy is consumed hourly (see Figure 1). AMI, however, is a piece of a larger puzzle. Collecting such an unprecedented amount of energy use data will be useful only when the data compiled is analyzed and turned into actionable, intelligent information. By applying analytics and statistical algorithms, utilities can process hourly smart meter data and determine which major electric appliances are used when (see Figure 2).
Presenting a customer’s disaggregated energy use helps him or her understand energy use habits and what he or she can control. Imagine a customer considering the benefits of a peak-time pricing program. A review of AMI data without disaggregation (see Figure 1) would lead a customer to decline the program because it appears that he or she continually uses too much energy during peak time–typically 2-7 p.m.–and the program would not benefit him or her. The same energy-use profile presented to the customer in a disaggregated manner (see Figure 2) provides the information needed to change how he or she uses energy and save money based on the peak-time pricing programs. For example, changing a pool pump timer and setting the thermostat for an early morning pre-cool of the house could make the peak-time pricing program beneficial.
A utility empowered with better information about customers’ energy use can take the conversation with customers even further. Once a customer is on the correct pricing program and his or her load is shifted from peak hours, the dialogue can move to reducing overall energy consumption and helping reduce global warming. Presenting a customer’s energy use in percentage by major appliance (see Figure 3) puts things in a different perspective for the customer and enables him or her to better correlate household activities to energy use. This enlightened view provides insight needed to take energy-saving actions, such as turning off a seldom-used stereo receiver or recycling an old garage refrigerator.
These examples should whet utility professionals’ appetites if they struggle to use the power that comes with AMI interval data. The machine learning technology used to break each customer’s daily energy use into major equipment loads can provide the following information:
- When air conditioning is used, and how much energy was used each hour,
- When electric heat is used, what the hourly energy was,
- The timer schedule of a swimming pool pump,
- Outside lighting timer schedule,
- When an electric dryer is used, and how large it is,
- When significant electric water heater use exists,
- Base load resulting from refrigeration and home electronics, and
- Energy use lifestyle (working during day, at home, kids in school, snowbirds, vacation home, etc.).
For a utility, this knowledge about customers’ energy use allows for ongoing, meaningful dialogue (instead of a one-time message). In addition, this knowledge enables utilities to deliver custom programs and messaging that will save customers significant money and help reduce their environmental footprints.
More Knowledge Enhances Program Delivery Success
The deployment of energy efficiency, demand response and time-of-use pricing programs is accelerating across North America. A critical task before program implementation is to determine the potential benefit and best adoption strategy. Energy use disaggregation can play a critical role during planning and can provide valuable decision support information. Imagine knowing exactly how many homes have swimming pools before launching a variable-speed pool pump program. Imagine knowing exactly which office buildings have data centers inside before designing an outreach strategy for virtual servers. Wouldn’t it be good when planning a demand response program to know which homes have air conditioning and which homes use it during peak periods?
From a communications perspective, progressive energy companies are abandoning traditional bill stuffers. They’re moving toward personalized on-bill messages, energy efficiency packages that are bundled specifically for each home and dashboards for customer service representatives to know customers’ needs when they call. The enabler of these innovative program delivery approaches is detailed and meaningful knowledge. The output of the latest energy disaggregation technology can be an integral component of the transformation from generic outreach to personalized, targeted customer communications.
California is mandating that by 2012 each home be provided with raw energy use data via a Web portal. Global warming acceptance and an environmentally conscious administration in Washington, D.C., are driving the same actions in most other states. Empowering consumers to make more informed energy decisions will require more than presenting raw energy use data; it will take a continuous dialogue about energy use with each customer. Leading utilities will take advantage of the latest machine learning technologies to forge the way toward this customer communications transformation.
The market size for energy-use analytics is predicted to grow at the same rate as smart meter deployments. The market is estimated at $40 million in its infancy today and is expected to grow 30 to 40 percent a year during the next decade. Utilities that deploy AMI while using the powerful capabilities of analytics will have a much higher success rate for customizing demand response, energy efficiency and pricing programs that will be embraced by their customers.
“Framework for Analyzing Separation Distances between Transmission Lines in Wyoming” PDF: http://icfi.com/docs/wyoming-transmission.pdf
“Wyoming Collector and Transmission System Conceptual Design” PDF: http://icfi.com/docs/wyoming-collector-transmission-final.pdf
Michael Madrazo is the founder and president of Detectent. The company helps utilities increase the efficiency of their revenue protection efforts by implementing proactive theft detection tools and services. For more information visit http://detectent.com or call 760-233-4030.