By Cyient senior marketing manager Asia Pacific Nathan Sawicki
Utilities are investing heavily in distribution management and monitoring infrastructure to collect massive amounts of high-velocity data from millions of smart meters, and a variety of data from sensors and external sources. Smart meters enable collection of data at smaller intervals, remote monitoring, and two-way communication.
Tapping into this data streaming in from tens of thousands of smart meters and integrating it into the business operations of utilities can provide several operational and business efficiencies, including better load forecasting from more frequent interval meter readings, theft detection, and improved response time to outage. These business efficiencies translate into economic and competitive advantages for smart utilities.
Business challenges facing utilities
Utilities are on a mission to minimise the impact of costs even while they prepare themselves for the regulatory changes and technological evolutions expected in the future. Some of the key challenges they face can be classified broadly as:
Aging Assets: Utilities are facing the double threat of aging assets along with the growing need for extracting more life and performance from the available assets.
Distributed resources: In a utility, energy generation could be distributed to multiple units. Integrating and managing these poses a significant challenge.
Regulatory controls: Regulations pertaining to utility supply and pricing are under constant scrutiny and are liable to change.
Demand response: To ensure demand response, the utility will require the ability to co-ordinate the demand-response events by monitoring and measuring real-time consumption data.
Geographic concentration of revenues: Most of the utilities have uneven distribution of revenues across geography because of which they are vulnerable to economic fluctuation in certain regions where revenue is concentrated.
Why meter data analytics
Smart meter analytics result in several business benefits both for the provider and the consumer. While it ushers in operational and business efficiencies for the retailer by monitoring consumptions and optimising peak demand in near real-time, consumers stand to benefit due to greater transparency and responsive information that will allow them to monitor and conserve energy usage.
Evolution of meter data analytics
Traditionally meter data has been primarily used for monthly billing and settlement. With the new meter data management systems being deployed around the globe, this opens up the possibility for a wide range of analytics on monthly consumption and billing data. In the recent past, energy companies have started to realise the potential of deriving actionable insights from smart meters and are looking to use these insights to improve business outcomes.
The big data revolution in smart meter analytics
As per an estimate, the electric utilities possessed 194 petabytes of data in 2009 and more terabytes of data has been arriving ever since. With vast quantities of data becoming available, it is not surprising the Big Data revolution has reached the utilities industry. Big Data technology platforms can collect, analyse, and visualise information required to answer questions that were never asked before.
Smart meter data, when combined with distribution network characteristics such as weather and other operational information, can proactively address service delivery issues, improve workforce efficiency, and enable utilities to offer demand response and energy efficiency programs to their customers.
Utilities are increasingly adopting these solutions. Several leading utilities have set an example by partnering with technology providers who can effectively convert smart meter data into tangible and actionable insights, in order to deliver better results to diverse stakeholders.
Choosing the right solution
Big Data from smart meters and sensor data from networks represent a potential gold mine for utilities looking to gain better insight into operations and customer relationships, and innovate in an increasingly challenging industry.
Choosing the right solution is extremely critical in this constantly evolving market. When it comes to building analytics around smart meter data along with other Big Data sources in a utility, the first solutions articulated in the above solution roadmap from head-end systems and MDM systems are eliminated right away because they are capable of only historic data analytics and cannot process real-time data. That leaves us with the other two options:
Option 1: COTS products for smart meter analytics
A small number of COTS products for smart meter analytics are available from leading vendors, and a few more are set to hit the market in the next couple of years. COTS solutions are generally preferred for their proven technologies, ease of implementation and quick ROI. However, we need to exercise caution while adopting COTS products for rapidly evolving technologies.
Option 2: Enterprise big data platforms
Enterprise big data platforms offer comprehensive analytics solutions with enterprise-wide scalability at comparatively lower cost, enabling utilities to leverage the business benefits of smart meter analytics.
Though enterprise big data platforms have traditionally been used to process only smart meter data as input, it can take data from other systems like SCADA to enrich event processing and build algorithms for generating insights. Depending on the business context, the individual operational systems can send data to the platform and get actionable insights out of it.
Smart meter data captures a wealth of information, which can be combined with other key data in the utility business to improve efficiencies, customer experience, and business results. It also helps consumers save money and help with energy conservation, especially during peak periods, and helps utilities prepare for better regulatory compliance.
Considering the rapid evolution of technology in big data analytics, it would be appropriate to start small with a platform that is capable enough to collect data, process it, and generate insights from required functional and operational datasets within the utility. However, utilities need to ensure that they have an able system integration partner who can support their analytics journey from inception to the realisation of enterprise-wide big data analytics.