Asset-intensive and field-force-driven utilities are taking a major shift with the advent of smart meters, sensors, intelligent devices and IoT-based systems. However, these enterprises are facing challenges in managing enormous volumes of data generated by assets, multiple MDMs, ERP/CRM and SCADA systems. If leveraged efficiently, this data can provide a better understanding of consumers, assets, and demand and supply operations. However, this seems to be an impossible mission with the traditional data management systems that are being used across utilities.
What are the problems at hand?
The main issues include:
- No real-time visibility to track and monitor consumption data, which would help utilities and end consumers be aware of their wastages and associated costs.
- Absence of a scalable data management system for utilities and meter providers, so storing and managing the humungous volume of data records collected from millions of meters is a challenge.
- No system to send alerts/notifications to report leakage, theft, overconsumption and utility loss, whenever it happens. Hence, utility providers and consumers are unable to take any actions to overcome the problems.
- Lack of proactive maintenance and remote asset management systems to manage the condition and working capability of assets.
- No predictive intelligence system to predict events, failures and outcomes for enhancing operational efficiency.
Therefore, utilities are facing an incessant need to reinvent their operations and realize the power of data systems. Consider a scenario wherein a utility gets to know about the failure of assets and operations well in advance. This would help the utility take corrective actions and measures or plan its pre-maintenance activities before the event occurs. However, utilities can realize this business value by adopting the prescience and predictive analytical capabilities, which would help them optimize asset performance, reduce downtime errors and enhance customer satisfaction.
The analytics solution
Research says that 32% of the traditional utilities are revamping their existing data management systems with advanced data analytics solutions that transform humongous amounts of data into intelligent insights and actions. They enable capabilities to monitor AMI/AMR systems, get real-time insights on utility distribution and consumption, optimize utility consumption and discover anomalies. Also, utilities can promote consumer awareness and enforce energy conservation by gleaning insights from consumers’ monthly utility usage patterns.
While serving its water utility customers, a smart meter manufacturer and utility service provider encountered several data systems-related challenges. However, it addressed all these challenges by implementing a scalable and robust smart metering data analytics solution which:
- Manages millions of smart meters, which send billions of records every year
- Provides real-time visibility of utility consumption, meter health, leakage/theft, hourly consumption data, etc.
- Has a notification engine to send consumers text-message alerts, alerting them on abnormally high usage or leaks
- Generates real-time insights from meter data to prevent over-consumption, leakage or wastage of utility
- Has every minute of utility consumption tracking and visibility on a dashboard for the operations team
Several Azure components such as IoT Hub, Event Hub, SQL DB, HDInsight, machine learning and stream analytics have been leveraged to architect this solution. While this particular solution addresses specific water utility business problems, similar analytics solutions can be developed to address the problems of the entire energy and utilities market.
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