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Smarter Revenue from Smart Grid Introduction

Google charges zero cents to us when we do a Google search or check our Gmail or use Google maps, but still the company makes a lot of money. As we all know most of Google's revenue is generated through personalized advertisement programs like 'AdWords' and 'AdSense'. When a user searches for something, Google stores the search predicates and maps them with the IP address of the computer. The combination of recent search predicates and web pages visited from that IP address determine the type of ads to be displayed to the user. So if you have recently searched for terms like 'Holiday' and 'New York', the next few web pages may contain advertisements of hotels in New York. In a nutshell, Google uses the browsing history of the user to push advertisements.

Why can't smart grid programs take a leaf from Google and use the selective electricity usage details (of course with the due consent of the customer) for personalized advertisements. This will enable the utility company to gain good revenue and offload some of the electricity bill from the customers. In that case if the customer has an old air-conditioner consuming a huge amount of energy and not chilling properly, that data can be used to show some advertisements of energy efficient AC to the user. This article throws light on the type of data that can be extracted from the grid and how it can be mined to select advertisers.

Source of data

The advent of smart grid will enable the utility to have the detailed information on consumption patterns of the households. This information can be used to infer some of the lifestyle trends of the people living in that house. For example, lesser consumption in the evening can imply that people return home late. A higher consumption on weekends might mean that the persons are not moving out in the off time. Complex pattern matching algorithms can be used to mine even more granular levels of data.

The smart appliances can be sources of a lot of data as well. Their data can contain details of the equipment, frequency of usage, energy consumed by the equipment etc. There can be a specific set of data for a particular appliance. For example, a prominent manufacturer had come up with a smart fried which will let the user scan the grocery bill and that data will be used to identify how many days the food has been stored inside the refrigerator. So such data can reflect the grocery list of the consumer, food storage baits etc. Similarly smart televisions which are connected to internet can provide details of TV viewing frequency, channels watched, genre of music listened etc.

All these data, when combined, can really reflect on the life-style of the people living at the house. Proper data mining can confirm their activities, habits and preferences etc. These data can be as powerful and informative as the data used by Google to display personalized advertisements to the users. Utility companies can mimic what Google does and push the required advertisements to the users.

Mode of advertisement

Most of the smart appliances come with an LCD display panels and that can be a potential placeholder for advertisement. Even though initially it would look like a bit of clutter, but it will take a few days only to get acquainted to, just like we are accustomed with advertisements on Google search page. And advertisements are not all evil. They can contain interesting information, tips, discounts etc. and also ensure a lot of saving for the customers in their utility bills.

Another interesting advertisement cocktail can be mixing the electrical usage data with that of an online advertiser like Google. In the smart grid era, the household can be identified by some unique IP address. When anyone from the home uses Google search, Google tracks the search tags originating from that IP and selects advertisements accordingly. If Google is provided with the electricity consumption info and smart appliance usage info originating from the same IP address, then the combined data will be even more informative to select proper advertisements for the users. So if you have an old air conditioner that has lower energy star rating (means consumes a lot of energy) and you do a Google search for 'How to reduce my electricity bill', then Google not only can put an ad of latest energy efficient AC in the web pages you visit, but also can customize the ad to display how much money you would save monthly if you use the new AC instead of the one installed at your home.

Security concerns

A good number of customer surveys on smart grid security reveal that the customers are skeptical of such intelligent monitoring devices, which transmit power usage information to the utility as frequently as every few minutes. These data can seriously compromise the privacy of the customer and can make them vulnerable to burglars, annoying marketers, insurance companies, and civil litigation's. A comprehensive report on smart grid privacy released by the National Institute of Standards and Technology (NIST) compiles a list of scenarios that consumers fear if their energy data got into the wrong hands. To preserve the privacy of the customers, some researchers have suggested sending anonymous power usage information to the utilities. Many of these similar security issues are already addressed in different domains of internet applications. That experience coupled with suitable cyber security laws will enormously help us build a secure and smarter grid.

Conclusion

Sometimes the opportunity lies within; it is just a step away from being utilized optimally. Data originating from the smart grids can be sources of smart revenue for the utilities. But for this to happen, the utilities must come up with strategies to win the confidence of the customer and reassure them of the fact that their privacy won't be compromised. At the same time there must be adequate investment and research to come up with proper security architecture for the grid.

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Copyright 2012 CyberTech, Inc.
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