THE OLD ADAG, “You can lead a horse to water .” appears to apply to utility customers with regard to their use of information from smart meters and their actions to use energy more wisely. If executives think their utilities can simply install smart meters and customers will automatically change the way they use energy, the findings of a 2011 IBM consumer survey provide dazzling evidence to the contrary. A total of 10,000 people in 17 countries were surveyed and IBM found a startling lack of knowledge. “Thirty percent didn't understand the basics of their energy bills,” said Michael Valocchi, vice president of IBM Global Business Services.
The report on the results of the study states that this lack of insight leads to decision-making processes that rely on the evaluations of trusted advisors, rather than on understanding the clear choices that the smart grid and smart meters offer to the customer .
In many cases, educating customers and giving them the information they need requires an intermediary – a company that sits between the customer and the utility. The role of the third party is to help manage the large amount of data collected by smart meters, analyze the data to produce actionable analytics on usage trends and correlations of usage with other factors, and present the information and findings in an easy understandable way to present to customers.
Of course, utilities could do this work themselves. However, they would need the staff and expertise to conduct the nuanced analysis, integrate the data into their existing systems, correlate the findings with other data, and present the information to the customer through, for example, a personalized web portal.
For help, utilities rely on software vendors such as IBM, Oracle, SAS Software, SAP and others, whose products are currently used in their back-office operations. Or they turn to third parties who add an extra dimension to meet these challenges.
An example of this intermediary role is an ongoing project between IBM and the government company of the Republic of Malta. The joint project discovered that the deployment of smart meters should be accompanied by new billing presentations that focus on the concrete steps to be taken to reduce consumption.
This is consistent with the findings of the IBM study, which found that customers in general, and younger customers in particular, were much more likely to change their consumption based on the approving decisions of their social circle of friends than on the basis of the traditional financial motivations of energy suppliers. So utilities need to find ways to tap into that social aspect of customer decision-making.
That's why IBM helped create an online smart energy portal that explains savings measures in easy-to-understand terms and provides the tools to measure a customer's energy savings progress compared to the progress of their peers.
Focusing on the social aspect of customer decisions is the strength of Opower, another intermediate company. Opower works with approximately 60 utilities, including eight of the 10 largest in the United States. It accesses data from more than 40 million smart and traditional meters and provides information to more than 10 million customers in North America.
Like IBM, Opower links data from smart meters with other information and then uses advanced analytics to develop customized messages for consumers. For example, using statistical algorithms and multivariable regression analysis that combines energy use, housing and weather data, Opower estimates the amount of heating and cooling energy used by each household without the need for an internal monitoring device. So instead of hearing that a household uses 10 percent more energy overall than its neighbors, customers may discover that they specifically use 30 percent more energy for heating and therefore may need maintenance on their furnace or new insulation, or turn their thermostat up. need to set a lower temperature.
Net als IBM koppelt Opower gegevens van slimme meters aan andere informatie en gebruikt vervolgens geavanceerde analyses om berichten op maat voor consumenten te ontwikkelen. Bijvoorbeeld, met behulp van statistische algoritmen en multivariabele regressieanalyse die energiegebruik, huisvestings- en weergegevens combineert, schat Opower de hoeveelheid energie voor verwarming en koeling die door elk huishouden wordt gebruikt zonder dat een intern bewakingsapparaat nodig is. Dus in plaats van te horen dat een huishouden in totaal 10 procent meer energie verbruikt dan de buren, kunnen klanten ontdekken dat ze specifiek 30 procent meer energie verbruiken voor verwarming en daarom wellicht onderhoud aan hun oven of nieuwe isolatie nodig hebben, of hun thermostaat op een lagere temperatuur moeten instellen.