Saving Energy, Smarter: MongoDB and Cedalo for Smart Meter Systems

The global energy landscape is undergoing a significant transformation, with energy consumption rising 2.2% in 2023, surpassing the 2010-2019 average of 1.5% per year. This increase is largely due to global developments in BRICS member countries—Brazil, Russia, India, China, and South Africa.

As renewable sources like solar power and wind energy become more prevalent (in the EU, renewables accounted for over 50% of the power mix in the first quarter of 2024), ensuring a reliable and efficient energy infrastructure is crucial. Smart meters, the cornerstone of intelligent energy networks, play a vital role in this evolution. According to IoT analyst firm Berg Insight, the penetration of smart meters is skyrocketing, with the US and Canada expected to reach nearly 90% adoption by 2027, whereas China is expected to account for as much as 70–80% of smart electricity meter demand across Asia in the next few years. This surge is indicative of a growing trend towards smarter, more sustainable energy solutions. In Central Asian countries, the Asian Development Bank is supporting the fast deployment of smart meters to save energy and improve the financial position of countries' power utilities.

This article will delve into the benefits of smart meters, the challenges associated with managing their data, and the innovative solutions offered by MongoDB and Cedalo.

The rise of smart meters

Smart meters, unlike traditional meters that require manual readings, collect and transmit real-time energy consumption data directly to energy providers. This digital transformation offers numerous benefits, including:

• Accurate Billing: Smart meters eliminate the need for estimations, ensuring that consumers are billed precisely for the energy they use.

• Personalized Tariffs: Energy providers can offer tailored tariffs based on individual consumption patterns, allowing consumers to take advantage of off-peak rates, special discounts, and other cost-saving opportunities.

• Enhanced Grid Management: Smart meter data enables utilities to optimize grid operations, reduce peak demand, and improve overall system efficiency.

• Energy Efficiency Insights: Consumers can gain valuable insights into their energy usage patterns, identifying areas for improvement and reducing their overall consumption.

With the increasing adoption of smart meters worldwide, there is a growing need for effective data management solutions to harness the full potential of this technology.

Data challenges in smart meter adoption

Despite the numerous benefits, the widespread adoption of smart meters also presents significant data management challenges. To use smart metering, power utility companies need to deploy a core smart metering ecosystem that includes the smart meters themselves, the meter data collection network, the head-end system (HES), and the meter data management system (MDMS). Smart meters collect data from end consumers and transmit it to the data aggregator via the Local Area Network (LAN). The transmission frequency can be adjusted to 15 minutes, 30 minutes, or hourly, depending on data demand requirements. The aggregator retrieves the data and then transmits it to the head-end system. The head-end system analyzes the data and sends it to the MDMS. The initial communications path is two-way, signals or commands can be sent directly to the meters, customer premise, or distribution device.

Figure 1: End-to-end data flow for a smart meter management system / advanced metering infrastructure (AMI 2.0)

When setting up smart meter infrastructure, power, and utility companies face several significant data-related challenges:

• Data interoperability: The integration and interoperability of diverse data systems pose a substantial challenge. Smart meters must be seamlessly integrated with existing utility systems and other smart grid components often requiring extensive upgrades and standardization efforts.

• Data management: The large volume of data generated by smart meters requires advanced data management and analytics capabilities. Utilities must implement robust data storage, processing, and analysis solutions to handle real-time time series data streams storage, analysis for anomaly detection, and trigger decision-making processes.

• Data privacy: Smart meters collect vast amounts of sensitive information about consumer energy usage patterns, which must be protected against breaches and unauthorized access. Addressing these challenges is crucial for the successful deployment and operation of smart meter infrastructure.

MQTT: A cornerstone of smart meter communication

MQTT, a lightweight publish-subscribe protocol, shines in smart meter communication beyond the initial connection. It's ideal for resource-constrained devices on low-bandwidth networks, making it perfect for smart meters. While LoRaWAN or PLC handle meter-to-collector links, MQTT bridges Head-End Systems (HES) and Meter Data Management Systems (MDMS). Its efficiency, reliable delivery, and security make it well-suited for large-scale smart meter deployments.

Cedalo MQTT platform and MongoDB: A powerful combination

Cedalo, established in 2017, is a leading German software provider specializing in MQTT solutions. Their flagship product, the Cedalo MQTT Platform, offers a comprehensive suite of features, including the Pro Mosquitto MQTT broker and Management Center. Designed to meet the demands of large enterprises, the platform delivers high availability, audit trail logging, persistent queueing, role-based access control, SSO integration, advanced security, and enhanced monitoring.

To complement the platform's capabilities, MongoDB's Time Series collections provide a robust and optimized solution for storing and analyzing smart meter data. These collections leverage a columnar storage format and compound secondary indexes to ensure efficient data ingestion, reduced disk usage, and rapid query processing. Additionally, window functions enable flexible time-based analysis, making them ideal for IoT and analytical applications.

Figure 2: MongoDB as the main database for the meter data management system where it receives meter data via Pro Mosquitto MQTT broker.

Let us revisit Figure 1 and leverage both the Cedalo MQTT Platform and MongoDB in our design. In Figure 2, the Head-end System (HES) can use MQTT to filter, aggregate, and convert data before storing it in MongoDB. This data flow can be established using the MongoDB Bridge plugin provided by Cedalo. Since the MQTT payload is JSON, it is ideal to store it in MongoDB as the database stores data in BSON (Binary JSON). The MongoDB Bridge plugin offers advanced features such as flexible data import settings (specifying target databases and collections, choosing authentication methods, and selecting specific topics and message fields to import) and advanced collection mapping (mapping multiple MQTT topics to one or more collections with the ability to choose specific fields for insertion). MongoDB's schema flexibility is crucial for adapting to the ever-changing structures of MQTT payloads. Unlike traditional databases, MongoDB accommodates shifts in data format seamlessly, eliminating the constraints of rigid schema requirements. This helps with interoperability challenges faced by utility companies.

Once the data is stored in MongoDB, it can be analyzed for anomalies. Anomalies in smart meter data can be identified based on various criteria, including sudden spikes or drops in voltage, current, power, or other metrics that deviate significantly from normal patterns. Here are some common types of anomalies that we might look for in smart meter data:

• Sudden spikes or drops: These include voltage, current, or power spikes or drops. A sudden increase or decrease in voltage beyond expected limits.

• Outliers: Data points that are significantly different from the majority of the data.

• Unusual patterns: Unusually high or low energy consumption compared to historical data or inconsistent power factor readings.

• Frequency anomalies: Frequency readings that deviate from the normal range.

MongoDB's robust aggregation framework can aid in anomaly detection. Both anomaly data and raw data can be stored in time series collections, which offer reduced storage footprint and improved query performance due to an automatically created clustered index on timestamp and _id. The high compression offered addresses the challenge of data management at scale. Additionally, data tiering capabilities like Atlas Online Archive can be leveraged to push cold data into cost-effective storage. MongoDB also provides built-in security controls for all your data, whether managed in a customer environment or MongoDB Atlas, a fully managed cloud service. These security features include authentication, authorization, auditing, data encryption (including Queryable Encryption), and the ability to access your data security with dedicated clusters deployed in a unique Virtual Private Cloud (VPC).

End-to-end solution

Figure 3: End-to-end data flow

Interested readers can clone this repository and set up their own MongoDB-based smart meter data collection and anomaly detection solution. The solution follows the pattern illustrated in Figure 3, where a smart meter simulator generates raw data and transmits it via an MQTT topic. A Mosquitto broker receives these messages and then stores them in a MongoDB collection using the MongoDB Bridge. By leveraging MongoDB change streams, an algorithm can retrieve these messages, transform them according to MDMS requirements, and perform anomaly detection. The results are stored in a time series collection using a highly compressed format.

The Cedalo MQTT Platform with MongoDB offers all the essential components for a flexible and scalable smart meter data management system, enabling a wide range of applications such as anomaly detection, outage management, and billing services. This solution empowers power distribution companies to analyze trends, implement real-time monitoring, and make informed decisions regarding their smart meter infrastructure.