As Finland prioritises sustainability and efficiency within its energy sector, the government is preparing to implement new regulations requiring energy companies to increase the frequency of meter readings significantly. These changes promote more precise energy management, enabling a more responsive and efficient energy grid, and this shift promises benefits for end customers. Still, it presents significant technical and operational challenges for energy companies as they adapt to an era of near real-time data collection.
The New Regulation: A Move To Near-Time Metering
Traditionally, Finnish energy companies have delivered meter readings at intervals that range from daily to hourly. The upcoming regulatory changes will push companies to capture and provide meter readings at much shorter intervals, at a minimum, at least once every six hours. While not real-time, it will be near-time throughout the day. This evolution means that while the data points processed stay more or less the same, the number of transactions will increase dramatically as the frequency of meter reads will skyrocket.
This regulatory push is part of Finland’s ongoing commitment to sustainable energy practices, aligned with the EU’s broader goals for a smart energy grid. A smart grid relies on highly responsive data that can offer timely insights, enabling everything from load balancing to more accurate demand forecasting. This inexorable move towards live data also allows consumers and utility companies to be more adaptable in their energy use, an essential consideration given the inevitable adoption of additional renewable energy sources.
The Benefits for Consumers
Improved Transparency and Control
With more frequent readings, consumers can access near-time insights about their energy consumption, allowing them to see exactly how much energy they use at any given time. This transparency makes it easier to adjust consumption patterns and reduce unnecessary usage. It provides insights via mobile apps or web portals, enabling customers to track and manage their energy in previously unimaginable ways.
Enhanced Energy Efficiency
Near-time insights into energy use can encourage more sustainable habits. For instance, displaying the higher costs associated with peak times incentivises customers to shift energy-intensive tasks to off-peak hours. This demand-side change in behaviour can reduce strain on the grid during peak demand periods and ultimately support Finland’s sustainability objectives.
More Accurate Billing
While not directly related, there is a linkage between metering frequency and billing accuracy. More frequent meter readings mean billing can reflect actual energy consumption much closer to the point of delivery and provide greater end-user visibility of the cause-and-effect of their energy-saving efforts; additionally, it involves fewer estimates and adjustments. Customers will experience fewer surprises in their bills – the dreaded “bill shock” – and better understand their charges, reducing disputes and improving overall satisfaction.
Greater Flexibility with Time-of-Use Tariffs
Energy companies are creating dynamic pricing models and leveraging time-of-use tariffs to incentivise customers to use energy during off-peak times; hourly and 15-minute price contracts are becoming widespread. When matched with the greater transparency that near-time metering provides, this flexibility creates an environment where customers can make informed decisions on their usage – particularly of energy-intensive uses – which, in turn, helps to balance demand on the grid, particularly during traditional periods of peak demand.
Challenges for Energy Companies
While the benefits for consumers are clear and continuing to develop, implementing these changes presents a formidable challenge for energy companies. Moving to a near-time data model, let alone real-time, significantly increases data processing requirements.
Data Management and Processing Infrastructure
The volume of data generated by more frequent meter readings is substantial. Increasing the reading frequency for each meter could mean handling tens of millions of transactions per day rather than thousands. Energy companies will need to invest in robust data management infrastructure to handle this influx of information. Processing data at such a scale requires advanced cloud storage solutions, sophisticated data pipelines, and real-time analytics capabilities. As a pioneer in transitioning to high-resolution meter reading, Finland needed to confront this challenge, and Hansen has worked with several early adopter DSOs to develop the appropriate solutions.
Data Security and Privacy Concerns
Handling sensitive customer data at such a scale raises the risk of cybersecurity threats. As the volume of transactions increases, so could the potential for breaches, making it essential for energy companies to invest in heightened security measures. Regulatory compliance, such as with GDPR, must be continuously monitored to protect customer data. Companies may need to encrypt data, restrict access controls, and establish stringent monitoring systems to detect potential breaches or vulnerabilities.
System and Meter Upgrades
Not all energy meters are equipped to handle frequent, near-time reporting. Many legacy systems may need to be replaced with modern smart meters capable of supporting high-frequency readings. Additionally, backend systems will require upgrades to handle the rapid ingestion, processing, and storage of data. This requirement represents a significant capital investment and operational effort for companies.
Skilled Workforce and Training Needs
The shift toward near-time data collection requires specialised skills in data science, cybersecurity, and cloud computing. Energy companies may need to expand their workforce or retrain existing employees to meet the new demands. This transition will require expertise in hardware and the software and analytical tools needed to make the most of the data collected.
Increased Operational Costs
The infrastructure, equipment upgrades, and skilled workforce add to operational costs. As they adapt to the new requirements, energy companies may face significant initial burdens in acquiring, implementing, and operationalising compliant systems. Balancing these costs while maintaining reasonable consumer pricing will be a delicate challenge, especially as the levels of investments scale.
The Road Ahead: Preparing for a Data-Driven Energy Future
As Finland prepares to implement these regulatory changes, the energy industry stands on the brink of a transformative period. The increased data flow promises to revolutionise energy management, driving efficiencies that will benefit both consumers and the environment. However, for energy companies, it also marks the start of a complex journey towards modernised infrastructure and processes.
To successfully navigate this shift, energy companies in Finland will need to embrace innovation, invest in infrastructure, and work closely with technology partners to build secure and scalable systems. Those who can adapt to these regulatory changes will comply with the new requirements and position themselves as leaders in Finland’s rapidly evolving energy landscape.
Ultimately, the shift to near-time metering is more than a regulatory change – it’s an opportunity for the Finnish energy sector to become smarter, more sustainable, and more customer-centric than ever before.
Real-world implications and How Hansen is Responding
As outlined above, the trend towards reading, metering, and reporting in near-time gives genuine advantages. However, the combined transition to highly granular and near-time energy measurement comes with costs: the processing workload for the quantity of data collected.
The massive increase in raw data collection is a recognised issue; generally, it’s yesterday’s news. However, for context, compared to earlier models, this volume can range from 24 measurement values a day for hourly readings and up to 96 when implementing 15-minute intervals (or to 288 values if moving a 5-minute interval). Having said that, data volume in and of itself is a relatively easy problem to solve.
Processing workload, however, is a different challenge. Previously, the AMI Head-End system might batch a series of metering point measurement values and send a file for processing daily. Driven by this regulatory change, values will be delivered much more frequently, significantly increasing the MDM’s transactional workload. Additionally, most MDM processes historically operated based on schedules, but many modern features have transitioned to event-driven. This evolution means that whereas we might previously transact one event per day per measurement, we now have many more, and this also applies to calculating the incremental value (difference between consecutive cumulative readings), validation, calculations and integrations to adjacent systems. For example, the measurement value delivery to billing is typically event-based and moving that integration to near-time would increase transactions with the billing system enormously.
When architecting our Cloud-native Hansen MDM, we foresaw the emergence of near-time – and, eventually, real-time – and specifically designed the processing workflows such that the performance and efficiency were substantially equivalent, regardless of the transactional frequency. This characteristic ensures that database performance is consistently high, whether processing single values for multiple measurements or multiple values for a single measurement. Additionally, when handling single values for input, our approach to processing uses a batch technique for greater efficiency.
An organisation’s MDM will be pivotal in transitioning to high-frequency measurements and near-time reporting by acting as a buffer between the AMI system and legacy solutions like billing, data warehouses, and more. Our experience with near-time (and real-time) measurements in production has proven that our techniques developed to handle data growth and change in the collection frequency are solid.
Hansen MDM enables a partial and incremental transformation from daily value collection to near-time collection. This capability is a significant enabler for energy companies, empowering the transition to high-frequency measurement value collection without requiring a simultaneous update of every system or platform.
Further reading: I recommend Gartner’s recently published Market Guide to Meter Data Management Systems for additional insight into near-time meter data management and the implications and opportunities for energy companies. This report defines and describes the current MDMS market and discusses one possible future direction, together with analysis and recommendations.
Riikka Kumlin, Senior Product Manager, Meter Data Management.
