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Data has become an integral part of every business since it helps the concerned stakeholders gather insights into critical aspects of business operations. Analyzing that data helps in predicting consumer behavior, channelizing the marketing efforts, and expediting the decision making process.

Companies in the energy (or utilities) sector are increasingly looking to harness the volumes of data stored in their back-end databases for maximizing business outcomes, improving operational efficiency, and providing better customer service. As per estimates, Smart utility companies are generating close to petabytes of data that is stored in their database warehouses. However, the data is primarily used for bill generation and performing preliminary data analytics.

In this blog, we look at how data analytics is a game-changer for the utility industry. We also deep dive into the nuances of smart meter data and how the AMI (Advanced Metering Infrastructure) data can be used in conjunction with other potential sources of data for gaining profitable business insights.

Table of Contents

Utilities – Riding the Big Data Wave

As per IDC[1], the global Big Data and Analytics market is expected to grow 3.5 percent points faster than the other software markets during the forecast period 2019-2024. This is an indicator of the importance of big data in the ever-competitive software field.

Companies in industries like Fintech, Banking, Telecom, E-commerce, and many others are already using big data analytics by analyzing consumer patterns for providing more personalized & secure services. The smart utility industry is not far behind in leveraging the data available at their disposal.

With the advancements in Automated Metering Interface (AMI), Meter Data Management (MDM) software, and Utility Meter Data Analytics (MDA), utility companies are realizing the truest potential of ‘data’ and are on the constant look-out for harnessing the data. A majority of the Utility companies are already doing operational data analytics but more value can be unlocked by operationalizing this data.

Here are some of the many ways in which Utility Meter Data Analytics (MDA) are used by utility companies to:

  • Generate bill forecasts by analyzing consumption patterns
  • Introduce new products and services
  • Improve Customer Satisfaction Score (CSAT) by gathering and analyzing data derived from customer insights
  • Identify and fix meter asset issues in a more proactive manner with analytics, thereby resulting in fewer truck rolls
  • Improve Smart Grid reliability
  • Use Gamification techniques in the utility sector for personalizing the user experience
  • Combat revenue losses with analytics-driven revenue theft protection
  • Build data models for realizing demand program planning and TOU (Time Of Use) pricing

Though smart utilities are realizing the immense potential offered by Utility Meter Data Analytics (MDA), they face challenges processing the mammoth size of data (in petabytes) using aging in-house enterprise systems. This is one of the many reasons why the Energy and Utility sector is embracing Cloud Computing.

Data analytics offered by cloud-based platforms like Oracle CC&B[2] could be the possible gateway for utilities to leverage analytics for realizing benefits across the utility value chain. SAP IS-U is a solution from SAP’s industry specific solution for the utilities industry. Along with SAP R/3, the solution from SAP can be seamlessly integrated with the standard components of SAP.

Opportunities Galore – Smart Meters and Meter Data Analytics (MDA)

Utility companies have access to an enormous amount of data and events from a diverse set of sources like smart meters, in-home devices, customer accounts, transformers, and more. Critical decisions can be taken by applying big data analytics on the collective set of information read from different data sources.

Opportunities are endless when the data from AMI and MDM (Meter Data Management) software is jointly leveraged with data from external sources like weather sensors, Geographic Information Systems (GIS), etc. The result is improved revenues, better demand-forecasting, and growing customer satisfaction.

Here are some of the opportunities that are enabled through Utility Meter Data Analytics (MDA):

Efficiently-planned Demand (or Load) Forecasting

Demand (or load) forecasting is a vital aspect that helps the utility provider in arriving at important decisions such as decisions on purchase & generation of electric power, load switching, infrastructure development, and more. Overall, it helps the utilities provider in planning power distribution based on the customer’s (and/or area’s) historical information about power usage.

The utility company has access to the customer database, smart meter information, power usage patterns, and other information that is instrumental in demand forecasting. Data analytics at scale can aid the utilities provider in better planning to meet future power demands.

For example, the power demands of certain shopping malls could sky-rocket during weekends (in comparison to weekdays) and the utilities provider could accordingly perform a demand forecasting. Smart Meter Data Analytics adds tremendous value in super-efficient demand forecasting.

Identification of Unbilled Revenue

Smart meter events and power usage patterns give a holistic view of the customer’s energy usage. Drawing intelligence from the usage patterns and meter events help in detecting power thefts, avoiding chances of meter tampering, and identifying meter alert issues (by monitoring the health of the meters).

The efficiency of operations improves drastically with the granularity of the data. Hence, multiple reads at different intervals (Eg; half-hours) duration can be performed for gathering the data accumulated over-time and later analyzing the same using appropriate Data Analytics algorithms.

There could be scenarios where customers have active accounts (with the energy providers) but no recorded usage. The reverse is also possible – energy usage with no active accounts. There can be cases where customers have accounts for different utilities like electricity, gas, etc. but there are drastic differences in the consumption patterns.

Identifying Meter Quality Issues

The health of the smart meter is one of the fundamental requirements to ensure AMI reliability. By analyzing the historical meter (and billing) trends along with performing multiple system reads (ideally in HH durations), the meter health can be monitored from time to time. Using this mechanism, meter faults can be detected and fixed at a faster pace.

In case the meter readings are not delivered (due to meter or infrastructure issues), the system makes notes and does a detailed calculation of the performance statistics for the AMI system.

Outage Analysis and Prevention

Truck Rolls is a common term that refers to sending utility workers (or linemen) in trucks for fixing power outages, fixing transmission and distribution asset issues, and more. As per estimates, the average cost of a truck roll could range anywhere between $150 ~ $500.

With the advent of Smart Metering and AMI, utility providers have been able to bring down truck rolls; as workers no longer have to visit the customer’s premises for reading the meter data. However, the costs can be further brought down by using analytics for detecting (and/or) preventing outage events.

Through data analytics in utilities, the provider can get detailed information on the exact equipment that is the cause of the outage. Along with it, the provider can predict the impact of the outage (i.e. approximate number of customers impacted by the outage).

Analyzing the historical data helps in detecting anomalies thereby minimizing the percentage of false positive(s). This essentially leads to fewer truck rolls resulting in greater savings for the utilities provider.

The outage event reports are helpful for utilities in understanding the overall impact of the outage and finding ‘exact’ problem areas in the power distribution network. Utilities can then limit the areas that could be impacted by outage by isolating the areas of high impact and addressing the outage problem at the earliest.

Improved Energy Efficiency

With data integration and data analytics, utilities can track as well as estimate energy usage down the asset level. This helps in an in-depth understanding of the consumption patterns, along with providing essential indicators like energy and cost calculations that aid in optimal utilization of energy assets.

Utilities usually have a Time-of-use (TOU) rate plan where the rates differ  according to factors like time of the day, day type (weekday/weekend), season (summer/spring/etc.), and more. Normally higher charges are levied during peak demand hours and rates are lower during off-peak hours. With access to numerous data points like customer information, consumption patterns, and others; utilities can make informed decisions on the right time for kicking-in the appropriate rate plan.

This also provides an opportunity for utility companies to influence the consumption pattern of the consumers. By analyzing consumer’s consumption patterns over a brief period of time, utility companies can provide relevant tips (or insights) on how the concerned customer can lower the energy consumption. This results in improved energy efficiency, thereby helping in the reduction of carbon footprint.

Utilities and Big Data – The Road Ahead

Utility Meter Data Analytics is playing an instrumental role in shaping the smart meter eco-system. However, there still lies immense opportunities in unearthing an incredible amount of value from the different sources of data.

As per ACEEE (American Council for an Energy-Efficient Economy) paper titled Leveraging Advanced Metering Infrastructure To Save Energy, utilities are under exploiting AMI capabilities thereby missing an opportunity to deliver more value to their customers and systems. AMI data can further help utilities and third-parties create better, more compelling, and more cost-effective demand-side offerings.

Utilities need to make the most of AMI data integrated with weather data, GIS data, premise data, transmission and distribution data, and other sources of data that can add value to utilities and customers alike. Operationalizing this data can result in improving the accuracy of demand-forecasting.

Many utilities are on the look-out to introduce new products & services that are devised by gathering insights from the AMI data. For example, using the AMI data; it is now possible to detect the number of Electric Vehicles (EVs) on the grid and figure out the exact locations for putting the charging stations. It is also possible to detect the type of charger (i.e. low/medium/high energy) used by the customers and subsequently engage with them by offering the right kind of charging equipments. This would result in improving the overall efficiency of the Electric Grid, along with providing the customers with the right kind of charging equipments to charge their EVs.

To summarize, here are the three major benefits offered by Smart Meter Data Analytics for diverse stakeholders in the energy eco-system:

Customers – More personalized and detailed energy information that helps in reducing energy consumption.Utilities – More in-depth energy information that helps in reducing costs, improving Grid efficiency, and reducing truck rollsEnergy Retailers – Provides an opportunity to reduce customer churn, gain customers by introducing new products & services, and improve the CSAT (Customer Satisfaction) Scores.

TekGeminus has helped utility providers in unlocking the potential offered by Meter Data Analytics. Reach us at [email protected] to unleash the power of AMI and Data Analytics.


[1] https://www.idc.com/getdoc.jsp?containerId=US46760920

[2] http://www.oracle.com/us/industries/utilities/big-data-analytics-customer-wp-2075868.pdf

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