|By Kevin Benedict||
|December 8, 2012 09:00 AM EST||
Every Tuesday I publish a newsletter entitled M2M News Weekly. I try to find all the interesting data that is reported on The Internet of Things and M2M (machine-to-machine) each week and then share it with a link to the original article. This week one of those items was Cognizant's Smart Meter Management Platform (SMMP). This platform enables utilities, using a smart grid, to turn on and off the utilities remotely for homes and businesses based on their status, plus a lot of other interesting things. I am the Head Analyst for SMAC (Social, Mobile, Analytics and Cloud) for Cognizant, so this news was particularly interesting to me. There is a PDF available on this topic here.
The SMMP solution utilizes an industry standard called MultiSpeak for integration between the smart grid, SMMP and a utility's ERP. Here is the clever part, the ERP is remotely turning on and off electricity based on information in the ERP. However, this is only the start. The vision for smart grids is much larger. Here is how Wikipedia explains it, "A smart grid is an electrical grid that uses information and communications technology to gather and act on information, such as information about the behaviors of suppliers and consumers, in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity."
Once you have a smart grid in place you have access to massive amounts of data from all the smart meters. The next question is what to do with the data? Going back to Wikipedia's description you would:
- improve efficiencies
- improve reliability
- improve utility economics
- improve the sustainability of production and distribution
That is great for the utilities, but the vision is also to make this Big Data available to customers as well. That way end customers can more effectively manage their own energy consumption. Smart grid data can be analyzed and made available to end customers through web portals and even mobile applications that enable people to look at real-time energy consumption. Once the real-time data is available, the next step is to enable end users to access their facilities management software and/or home automation systems and to be able to adjust energy consumption remotely.
Utility companies are also interested in looking at the overall smart grid data. They may want to adjust their prices based on the Big Data analysis and charge more for peak hours than off peak hours with the intent of influencing the consumption and behavioral patterns of their consumers. If they can motivate consumers to reduce their energy consumption during peak hours, then the utilities can support more customers without developing more energy generation capacity. This has the potential of saving utilities billions of dollars. Now that is an ROI!
I have also read about companies allowing utilities to manage the operations of large numbers of irrigation pumps in California, so they schedule them to run at different times rather than all at once. This enables the utility to even-out the energy consumption rather than having such high peak consumption times.
The challenge, however, is that many utilities have used stimulus money to implement smart grids, but they have not completed the solution by connecting remotely to the smart meters and then integrating the smart grid with analytics and their back office solutions. Without communication and integration the smart grid is not smart.
Big Data and business analytics play a big role in the smart grid vision. Once a smart grid is operable, real-time analytics need to be watching it for signs of meter tampering, communication problems and effectively managing the distribution of electricity. This is the role Cognizant's Smart Meter Management Platform plays.
I have this vision of using gamification so neighborhoods could compete for lowest average energy consumption per residence in order to win awards. That would be very cool!
I wanted to gather all of my Internet of Things (IOT) blogs into a single blog (that I could later use with my University of San Francisco (USF) Big Data “MBA” course). However as I started to pull these blogs together, I realized that my IOT discussion lacked a vision; it lacked an end point towards which an organization could drive their IOT envisioning, proof of value, app dev, data engineering and data science efforts. And I think that the IOT end point is really quite simple…
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