Busiess Process Services White Paper Smart Ways to Implemet Smart Meters: Usig Aalytics for Actioable Isights ad Optimal Rollout
About the Authors Sumit Joshi Sumit is part of the Aalytics ad Isights team at TCS, where he is resposible for buildig solutios for cliets i the eergy, resources, ad utilities idustries. He has over seve years of experiece i various fields such as cosumer products, retail, bakig ad fiacial services, ad eergy, resources, ad utilities. Sumit is a qualified Chartered Accoutat ad holds a MBA i Fiace from IBS, Hyderabad, ad a Bachelor s degree i Commerce from St. Xavier s College, Kolkata. Prithwis De Prithwis is part of the Aalytics ad Isights team at TCS, which builds solutios for the eergy, resources, ad utilities idustry. He has over 16 years of experiece i high-ed aalytics, research, cosultig, market research, solutio developmet, service delivery, ad operatios maagemet. Prithwis has worked i various domais icludig bakig ad fiacial services, cosumer goods, retail, telecom, maufacturig, ad eergy, resources, ad utilities. His articles have bee published i various jourals, books, ad ewspapers. He holds a PhD i Applied Ecoometrics.
Smart meters ad smart grids provide utility compaies with remarkable capabilities ad opportuities to exploit Big Data aalytics for gaiig valuable isights ito the busiess. The use of smart meters beefits all stakeholders utility providers, cosumers, ad the eviromet. However, a sigificat challege i leveragig this techology is the iability to tap ito the large volume of data geerated to attract ad retai customers. Effective utilizatio of the data ca help predict eergy demad with a greater degree of accuracy, ad hece reduce the overall cost burde for both utilities ad cosumers. This paper examies the issues that ca arise i the process cycle from istallatio to actual use of smart meters by ed customers. It suggests aalytical solutios that ca be adopted by utilities to overcome these challeges ad reap log-term busiess beefits from deployig smart meters.
Cotets 1. Itroductio 5 2. Cocers i deployig ad usig smart meters effectively 5 3. Desigig a rollout pla for maximum busiess impact 6 4. Leveragig actioable isights: Miig the vast data to derive isights for better busiess outcomes 7 5. Reducig the cost imbalace usig smart meters 7 6. Coclusio 8
1. Itroductio Smart meters are electroic measuremet devices used to operate ad regulate cosumers utility systems ad commuicate cosumptio data to the utility compay as well as the cosumer for billig purposes. Over the last few years, utility compaies have used such meters to provide accurate eergy cosumptio ad billig data to their cosumers. Istallig smart meters offers several beefits for both sets of stakeholders, icludig accurate billig, improved cotrol over eergy cosumptio, ad potetial savigs i eergy. The major beefits that each of these stakeholders ca derive are listed i Figure 1. Cosumers Detailed feedback o eergy use patters ad cosumptio Improved cotrol over eergy costs Accurate bills with o more readig errors Improved ad icreased pricig optios Improved outage restoratio Improved quality of data o power Utility Providers Elimiatio of maual meter readig Quick moitorig of electric system Efficiet use of power resources Real-time useful data for balacig electric loads ad reducig power outages Better capacity plaig Dyamic pricig based o demad Eviromet Reduced cosumptio ad efficiet distributio to reduce wastage Reduced pollutio from icreasig power geeratio Figure 1: Beefits accruig to various stakeholders from smart meter deploymet 2. Cocers i deployig ad usig smart meters effectively The beefits of smart meterig are ot limited to accurate billig. It presets a opportuity for utilities to mie large volumes of real time data available i smart meters ad gather ew isights to drive busiess beefits. For example, orgaizatios ca roll out customized offerigs to their customers based o their behavior ad usage patters, ad thereby improve customer satisfactio. 5
Despite these beefits, utilities face sigificat challeges both i deployig as well as i usig smart meters. It is importat for utilities to recogize these challeges ad develop cocrete plas to overcome them. The cocers for utilities iclude those idetified i Figure 2. Aalytics for smart meterig Areas of cocer Rollout Pla Vast Data Cost imbalace Objective Idetify locatios for implemetatios, ad use aalytics to prioritize locatios based o projected returs Idetify customers most likely to adopt ad utilize smart meters advatageously Gai isights to realize log term beefits for busiess ad cosumers Reduce cost imbalace through proper demad forecastig Figure 2: Cocers ad cosideratios for deployig ad usig smart meters effectively These challeges ad cocers eed to be effectively addressed i order to maximize retur o ivestmet ad optimize busiess outcomes. I the followig sectios, we have outlied differet ways i which these cocers ca be met. 3. Desigig a rollout pla for maximum busiess impact Aalytics are ot oly required after the implemetatio of smart meters but ca also be of great help i the preimplemetatio phase. The right combiatio of aalytics i these two phases ca help icrease overall reveue ad also ehace market peetratio, customer acceptace, customer loyalty, ad brad reputatio. Aalytics ca be applied i two stages for the implemetatio of smart meters: Stage oe: Statistical ad aalytical tools ca be used to idetify the right set of geographies to be targeted first. These geographies ca be picked based o samplig techiques ad various other criteria such as stratified (regio, umber of appliaces, ad household size), disproportioate (over-sampled groups with higher variace), ad radom (equal chace of selectio) samplig. Stage two: Oce the geographical locatios have bee idetified, aalytics ca help idetify a select group of customers to be targeted withi the specified locatio. The select group of customers may iclude those who cotribute the most to reveue or the oes who are most likely to chur. By targetig such customers, the utility ca icrease returs ad ehace customer retetio. 6
4. Leveragig actioable isights: Miig vast data to derive isights for better busiess outcomes Smart meterig ca provide useful ad actioable isights to revitalize a busiess. Cosiderable volumes of data are ow easily available with the implemetatio of smart meters, presetig a huge opportuity for utility compaies to ehace ed-customer service as well as improve busiess outcomes. Smart meter aalytics ca provide details o eergy use ad cosumptio to help cosumers reduce their bills, ad eable utilities to lower costs ad improve efficiecies. Eterprises are just begiig to idetify the potetial of aalytics that smart meter data is capable of producig. Isights such as variatios i volume of use relative to the time of use, appliaces impactig cosumptio, etc., ope up a wide rage of ew opportuities ad uses. Some of the aalytical opportuities available to busiesses due to the adoptio of smart meters iclude: Ehaced customer segmetatio: Smart meterig eables segmetatio based o various parameters like attributes of customers, cosumptio patters, etc. Oe of the key goals of smart grids is to allow cosumers to participate i the decisio regardig their usage of the utility, depedig o their priorities such as savig moey or [1] cotributig to the eviromet, etc. To help with this, orgaizatios ca provide cosumers with variable pricig plas based o the time of usage ad thus reduce demad durig peak times. Customer isights: Smart meterig provides istat aalysis of customers eergy cosumptio data at all levels of graularity ad dimesio as well as usage patters. Customer isights help the utility compay with capacity plaig ad eable it to offer differetial pricig for differet customers, or at various times of the day or the year (based o high ad low loads). Aalytics ca also help orgaizatios pla campaig activities targeted at specific customers, which i tur yield a better retur o ivestmet. Eergy kow-how: Peer compariso of customer data ca be carried out based o statistical predictios ad root cause aalysis of variace i cosumptio ad causal aalysis of loads. Ultimately, smart meters ca help the edcosumer effectively use utility services by maagig the flow i real time, eve remotely. Busiesses ca realize log-term beefits as a result of improved eergy efficiecy through real-time iformatio o eergy usage ad reduced eergy loss as well as icreased reveues through cross ad up-sellig aveues. 5. Reducig the cost imbalace usig smart meters Cost imbalaces for the utility are largely caused by a mismatch betwee demad ad supply, which affects the peruit cost of productio ad distributio. I the log ru, both surplus ad shortage are harmful for the busiess. This meas the utility should strive to meet the desired requiremet optimally. It is possible to do so by accurately forecastig demad through the use of predictive aalytics based o data that is gathered usig smart meters. [1] Sahil Thakur, Nikita Goel, Iteratioal Joural of Techical Research ad Applicatios, 2013, How Smart Grid Will Eergize The World, http://www.ijtra.com/view/how-smart-grid-will-eergize-the-world 7
Utility compaies, util ow, have so far bee usig basic demad forecastig techiques such as time series, causal models, liear tred lie, etc. However, these techiques are iadequate to cope with the growig complexity of demad fluctuatios ad the availability of extremely large volumes of data i the wake of smart meters. New forms of forecastig techiques employig predictive aalysis use treds to develop decisio trees ad provide complex predictios. Applicatio of predictive aalytics helps utilities: Process large volumes of data faster Idetify factors that lead to chages i load Aalyze the impact of each factor Uderstad the impact of weather o demad ad supply As the beefits of a predictive aalytics model kick i, other forms of aalysis (such as reactive or decisio based aalytics) ca be used to refie the results of predictive aalysis to drive better decisio-makig. 6. Coclusio The utility idustry is facig a paradigm shift i trasitioig from traditioal to smart meters. While may compaies deployig smart meters might be tempted to focus purely o fixig issues ad esurig right delivery to their customers, there is a huge opportuity for busiesses to use aalytics from the very outset. Use of aalytics ca help solve some of the issues faced by utility compaies by eablig a more graular view of iformatio i real time. Smart meterig without the support of aalytics provides oly limited beefits. Whe combied with data aalysis, smart meterig ca offer a wide rage of beefits across the value chai that icludes utility suppliers ad distributors, as well as cosumers. It ca help all the stakeholders better equip themselves to address the challeges arisig out of smart meter implemetatio. The bottom lie is that smarter isights from smart meters ca help utilities icrease efficiecy, reduce costs, ad provide better services to customers. 8
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