Editor's Note: This article is adapted from our technical webinar on managing high-volume IoT data in the energy sector. It explores trends like EV adoption and battery optimization, and shares real-world lessons from developers building scalable monitoring and analytics systems.

As energy infrastructure modernizes, the explosion of IoT data from renewables, batteries, EVs, and microgrids is overwhelming legacy historian systems. TimescaleDB, a time-series extension of PostgreSQL, delivers the scale, flexibility, and SQL-native performance modern energy applications demand.

The Changing Role of Data in Energy Innovation

Over the last decade, the role of data in the energy sector has fundamentally shifted. What was once a record of operations is now a real-time feedback loop powering everything from predictive maintenance to decentralized grid orchestration.

This shift—driven by the rise of renewables, EVs, batteries, and microgrids—has created a surge in high-frequency, high-volume IoT data. But collecting data isn’t enough. The organizations leading this energy transition are the ones extracting real-time insights at scale, turning raw data into operational advantage. For engineering teams, that means rethinking legacy infrastructure—and adopting purpose-built tools designed for today’s data reality.

Infographic highlighting innovation in the energy sector, showing how renewables have transformed generation, batteries are the new backbone, EVs have changed demand, and microgrids are decentralizing power.

Four Fundamental Changes Reshaping the Energy Sector

As renewable energy sources grow, electric vehicles proliferate, and decentralized microgrids emerge, the industry is generating unprecedented volumes of time-series data. Managing this data effectively has become critical for improving efficiency, ensuring reliability, and meeting sustainability goals.

Let’s explore the intersection of IoT and energy: what’s happening in the industry, the challenges of working with high-velocity data, and how Timescale provides powerful solutions for energy innovators.

1. Renewables Have Transformed Generation

The shift to renewable energy represents the most significant change in power generation since the adoption of AC/DC standards. Unlike traditional power plants that can increase output on demand, renewable sources like wind and solar are inherently intermittent.

Wind turbines only generate when the wind blows, and solar panels produce power when the sun shines—regardless of when consumers need electricity. This fundamental mismatch between generation and consumption has transformed grid operations from simple supply-demand balancing to a complex orchestration of storage, distribution, and consumption patterns.

This shift demands sophisticated data analytics to predict generation, optimize distribution, and balance loads in real time.

2. Batteries Are the New Backbone

Energy storage, particularly batteries, has become central to modern energy systems. Whether in grid-scale installations, commercial buildings, or EVs, batteries generate vast amounts of data that needs constant monitoring and analysis of multiple parameters that traditional systems weren’t designed to correlate. With the variability of renewable generation, energy storage has become essential. Batteries serve as the linchpin for storing excess power and releasing it when needed—whether at grid scale, in microgrids, in homes, or in electric vehicles.

The battery ecosystem has spawned numerous startups focused on:

Battery performance monitoring and optimization
Predictive maintenance
Battery repurposing and second-life applications
Storage-as-a-service business models

Batteries are critically important to the energy sector. They've become a focal point of the new energy industry, and we've got a lot of customers, like Octave, who have battery use cases.

3. Electric Vehicles Have Changed Demand Patterns

The rise of electric vehicles has fundamentally altered electricity demand curves. Traditional power consumption followed predictable patterns—peaking during business hours and early evenings, with significant drops overnight.

EVs disrupt this pattern, as many owners charge their vehicles overnight. Areas with high EV adoption now see flatter demand curves with increased nighttime consumption. This shift requires utilities to rethink generation schedules, infrastructure planning, and rate structures.

Moreover, each EV generates its own data streams—from charging patterns to battery health metrics—creating new analytics opportunities and challenges.

4. Microgrids Are Decentralizing Power Generation

The traditional centralized power grid is evolving into a network of interconnected microgrids. These localized energy systems can generate, store, and distribute power independently while maintaining connections to the main grid.

Microgrids might serve university campuses, military bases, remote communities, or even individual buildings. They often combine solar panels, batteries, and backup generators to ensure reliability while optimizing costs.

The centralized grid is giving way to a more distributed model, with microgrids generating, storing, and managing their own power. This decentralization creates new data management challenges that traditional systems weren't designed to address, requiring sophisticated monitoring and control systems to balance generation, storage, demand, and grid interactions.

But as the industry evolves, the tools many teams rely on haven’t kept up.

The Limitations of Legacy Historian Systems

Data historians are commonly used in industries and applications that require continuous monitoring, recording, and analysis of large volumes of time-series data. Energy and utility companies have long relied on purpose-built historian databases to capture operational data from industrial equipment. These systems excel at what they were designed for: reliably collecting sensor data in critical infrastructure environments.

Legacy historian databases struggle with ingestion rates beyond a few thousand records per second.

And when engineers try to build modern applications on this foundation, they encounter significant limitations:

Ingestion bottlenecks: Modern IoT deployments generate orders of magnitude more data than historians were designed to handle. Whether we're talking about 10,000 readings per second or a million readings per second, if you just throw those into SQL Server or standard Postgres, you will eventually have issues.

Crude data compression: To manage storage costs, historians typically compress older data by permanently discarding readings—losing potentially valuable information forever. In the world of historians, compression usually means downsampling... throwing away data points you might not care about.

Limited analytical capabilities: While excellent for basic trending, historians lack the analytical depth that modern energy applications require. SQL-based analysis—the lingua franca of data in other industries—is often unavailable or severely limited.

Isolated data silos: Historians typically exist in operational technology islands, making it difficult to combine operational data with business intelligence or customer information.

Expensive proprietary ecosystems: Many historians use proprietary data formats, query languages, and visualization tools—locking users into a single vendor's ecosystem and limiting integration options.

When Data Outpaces Infrastructure

The convergence of renewables, EVs, batteries, and microgrids has triggered an unprecedented surge in time-series data. Energy companies, utilities, and startups alike are capturing this data to power critical use cases. For example:

Optimize operations and reduce costs: Engineers are analyzing massive data streams to detect inefficiencies, improve uptime, and reduce waste.
Predict maintenance before failures occur: As seen with Octave Energy, real-time battery metrics help teams know exactly when to swap or repair a battery before performance drops.
Balance supply and demand in real-time: As generation becomes more intermittent, maintaining grid stability requires constant orchestration between what’s produced and what’s consumed.
Uncover new efficiency gains: From microgrid load balancing to smart metering, granular analysis enables better decision-making across energy systems.
Unlock new revenue models: A wave of energy innovators is building entirely new business lines—from energy arbitrage to storage-as-a-service—powered by real-time data insights.

But as opportunity grows, so do the technical demands.

This new era of energy data in the renewable era is:

High-volume: Ingesting millions of readings per second across thousands of sensors
High-frequency: Data arrives every second—or faster—in some applications
Mission-critical: Where uptime, accuracy, and trust are non-negotiable
Long-term: Historical analysis often requires storing and querying years of data

Infographic on energy data challenges in the renewable era, showing four dimensions: scale (300M+ daily readings), velocity (sub-second grid response), complexity (multi-format time-series data), and intelligence (real-time to long-term analytics).

Traditional historian systems and general-purpose databases simply weren’t built for this. They struggle to ingest at scale, compress without data loss, or run complex queries in real time. That’s why more and more engineering teams are turning to time-series databases purpose-built for demanding workloads.

Time-Series Data Management Strategies for Modern Energy Applications

So what does it take to succeed with energy data at scale?

From dozens of energy innovators who've rebuilt their data systems, we've distilled six essential strategies that consistently separate successful implementations from struggling ones. These aren't theoretical best practices—they're the real-world approaches we've seen drive performance, resilience, and scalability in modern time-series architectures.

Infographic titled “Strategies for working with time-series data,” listing six best practices: structure data for flexibility, optimize for high ingestion rates, handle late-arriving data, compress without loss, always index and materialize, and implement a data retention policy.

And while these examples draw from the energy sector, the strategies apply far beyond it.

Whether you're building systems for industrial IoT, smart infrastructure, connected devices, or any workload where scale, speed, and reliability matter—these principles hold. They are foundational best practices for any demanding application that relies on high-ingestion, high-frequency real-time systems.

1. Structure Your Data for Flexibility

How you model your data has profound implications for future analytics capabilities. The energy sector's rapid evolution means your data model needs to accommodate new sensor types, changing reporting frequencies, and evolving requirements. The schema you choose significantly impacts performance, storage efficiency, and query flexibility. Make these decisions early, as changing data structure becomes increasingly difficult as volumes grow.

For more information on database design and modeling, check out our Best Practices for Time-Series Data Modeling guide. It covers critical schema design decisions—wide versus narrow structures, single versus multiple readings per data point—all validated through real-world implementations.

2. Optimize for High Ingestion Rates

Energy IoT devices often generate thousands or even millions of readings per second. A database that excels at general-purpose workloads will falter under this load. As shown in our webinar demo, we ingested 300,000 records/second on a standard Timescale Cloud Service. That would be impossible with a standard relational database.

To reliably manage high-throughput ingestion at scale, your database architecture should incorporate strategies like:

Buffering to handle burst traffic
Batch processing for efficiency
Write optimization
Separation of ingest and query paths

3. Account for Late-Arriving Data and Modifications

In the real world, IoT data is messy. Devices go offline and later report batches of historical data. Readings sometimes need correction when sensors are recalibrated or errors are discovered. As much as we would like it to be, IoT data or energy data isn't immutable. Sometimes things do change:

Devices may go offline and later send backlogged data
Meters might require calibration corrections
Estimations may be replaced with actual readings
Data quality issues might necessitate adjustments

While dedicated time-series databases often struggle with this use case, TimescaleDB—with its foundation in PostgreSQL—handles late-arriving data and modifications gracefully.

4. Compress Data Without Loss

Most time-series solutions offer compression, but with a significant tradeoff: they permanently discard data points through downsampling. Storing years of high-frequency data can become prohibitively expensive without compression. The ideal approach:

Achieves high compression ratios (80-95%)
Preserves full data fidelity (no downsampling)
Maintains query performance
Operates automatically

Our approach is different. During the webinar demonstration, we showed compression rates of approximately 90% while preserving every single data point. For Octave Energy, compression reached 25x (96%) without sacrificing analytical capabilities.

5. Always Index; Materialize When Possible

Effective indexing is essential for query performance with time-series data. Additionally, pre-calculating continuous aggregations (materialized views) can dramatically improve dashboard and analytics performance. During our demo, we showed a query that initially took 17 milliseconds. After implementing a continuous aggregate (Timescale's materialized view implementation), the same query returned in just 2 milliseconds—nearly 9x faster.

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Query performance can be improved 10–100x with materialized views for common aggregations.

With massive datasets, proper indexing becomes critical:

Index time dimensions for range queries
Index device/asset IDs for filtering
Create continuous aggregates for common queries
Balance index overhead with query performance

6. Implement a Data Retention Policy

Not all data needs to be kept forever, and not all data needs to be on high-performance storage. A thoughtful retention policy can significantly reduce costs:

Define retention policies based on business needs
Automatically move older data to lower-cost storage
Maintain transparent access across storage tiers
Align retention with compliance requirements

How Timescale Addresses Energy Data Challenges

Timescale offers a unique approach to time-series data management that aligns perfectly with energy sector needs. Here are how some of our features align with the strategies discussed.

Time-Series Optimized PostgreSQL

Timescale extends PostgreSQL with specialized time-series capabilities that energy teams need without sacrificing SQL flexibility. By building on PostgreSQL rather than replacing it, energy data systems benefit from:

Optimized time partitioning: Automatically organizes energy data by time for efficient queries across massive measurement datasets
Full SQL capabilities: Run complex joins between meter readings and asset data using standard SQL—no proprietary query language required
Enterprise-grade foundation: Build on PostgreSQL's proven reliability while gaining time-series optimizations
Integrated ecosystem: Use PostGIS for geospatial analysis of distributed energy resources alongside time-series data in a single database

The result is a technical foundation that handles both high-frequency sensor data and complex relational models without compromise—giving energy engineers the analytical capabilities they need without managing multiple database technologies.

Hypertables for Efficient Time-Series Management

Run complex queries continuously, with near-zero latency, with Timescale. Under the hood, this is achieved by using hypertables—PostgreSQL tables that automatically partition your data into optimally-sized chunks based on time, enabling:

Write speeds up to millions of rows per second
Query performance that stays consistent as data grows
Simplified management with automatic chunk creation

Hypercore: Hybrid Row-Columnar Storage Engine

Designed for time-series workloads, TimescaleDB's Hypercore engine solves a fundamental challenge in time-series database design by combining row and column storage formats. This hybrid approach is particularly valuable for energy IoT systems that require both write-heavy operations and analytical queries.

Compression Metrics:

90-95% reduction in storage footprint
Lossless compression that preserves all original data points
Transparent query access to compressed data
Significant reduction in storage costs for energy monitoring implementations

Continuous Aggregates

Continuous aggregates provide an efficient mechanism for pre-calculating common energy metrics to significantly improve query performance in time-series data systems:

Specialized materialized views that automatically update as new energy measurements arrive
Full support for complex energy-specific aggregations (avg. power consumption, min/max voltage, percentile calculations for load distribution)
Performance improvements of 10-1000x for common energy monitoring queries
User-configurable refresh intervals to balance data freshness against system overhead

This approach ensures energy analysts and operators have access to up-to-date aggregate metrics without the performance penalties of repetitive calculations across massive energy datasets.

Tiered Storage

Timescale's tiered storage architecture provides a cost-effective solution for managing the complete lifecycle of energy data—from real-time monitoring to long-term compliance retention. Engineered for infinite low-cost scalability, tiered storage consists of the:

High-performance tier: rapid access to the most recent, and frequently accessed data.

Object storage tier: store data that is rarely accessed and has lower performance requirements. For example, to save old data for auditing or reporting purposes over long periods of time, even forever. The object store is low-cost bottomless data storage built on Amazon S3. You use it to avoid the higher costs and data size limitations associated with the high-performance tier.

No matter the tier your data is stored in, query it when you need it. Timescale seamlessly accesses the correct storage tier and generates the response.

Tiered storage diagram for PostgreSQL-based real-time analytics, showing hot data (uncompressed/compressed) in a high-performance tier and cold data in a low-cost, bottomless tier; SQL queries access all tiers transparently.

This architecture solves a critical challenge in energy data management: balancing performance requirements against storage costs. Energy teams can retain complete historical data for regulatory compliance, trend analysis, and audit purposes without compromising on query capability. Timescale handles the technical complexity of this storage hierarchy transparently. Queries spanning both tiers execute seamlessly without requiring developers to specify storage locations or manage data movement—the query engine automatically retrieves data from the appropriate tier based on time ranges and data age.

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Tiered storage is only available for the time-series and analytics services you create in Timescale Cloud. Tiered storage DOES NOT work on Self-hosted TimescaleDB or Managed Service for TimescaleDB.

Octave Energy: 26x Compression, Real-Time Analytics, and a Smarter Battery Future

How does a modern time-series approach translate to real-world impact? Belgium-based Octave Energy provides a compelling example.

Octave's business model is elegantly circular: they take EV batteries that have reached around 70-80% of their original capacity (the point when they're typically replaced), analyze their performance characteristics, and repurpose them into battery cabinets for commercial applications.

This second life for batteries is only possible through sophisticated data analysis. Octave processes high-volume streams of battery telemetry, capturing timestamped measurements such as voltage and temperature from every cell.

These readings are ingested from IoT edge devices and sent to their Battery Cloud platform, where they serve as the foundation for system safety, performance optimization, and predictive maintenance.

Initially, they built their platform on AWS Timestream.

"We initially used AWS Timestream in the early days of Octave, which at first seemed a natural choice to handle our time-series data since our cloud infrastructure was entirely built in AWS. However, we quickly realized we would need a more widely used, mature, and scalable solution." — Nicolas Quintin, head of Data at Octave

By migrating to Timescale, Octave saw these improvements:

25x data compression without sacrificing data fidelity
Significant query performance improvements for both real-time monitoring and historical analysis
Advanced analytics powered by SQL and specialized time-series functions

Grafana dashboard visualizing battery metrics using TimescaleDB and PostgreSQL, showing state of charge, DC current, voltage, power output, and temperature for real-time IoT monitoring

An Octave dashboard shared during the webinar revealed that they compressed 132 gigabytes of raw data down to just 5.1 gigabytes on disk—a direct operational cost savings while maintaining full analytical capabilities. Today, Octave's data platform continuously monitors battery cabinets deployed at customer sites, ensuring optimal performance while providing the technical foundation for their innovative circular business model.

Implementation

Whether you're building renewable monitoring systems, EV charging infrastructure, battery management solutions, or grid analytics tools, the right data foundation is crucial. Here's how to begin your journey:

Assess Your Current State: Understand your existing data architecture, identifying pain points and limitations.
Define Your Requirements: Clearly articulate your ingestion rates, query patterns, retention needs, and integration requirements.
Evaluate Solutions: Consider both commercial historians and open-source time-series databases.
Start Small: Begin with a pilot project that demonstrates the value of modern time-series strategies.
Scale Thoughtfully: As you expand, continuously refine your approach based on real-world performance.

Moving Forward: Modernizing Your Energy Data System

These webinar-extracted insights highlight how technical teams are moving beyond traditional historians to implement modern time-series data management systems that drive energy innovation. Whether you're building renewable monitoring systems, EV charging infrastructure, battery management solutions, or grid analytics tools, the right data foundation is crucial.

Ready to take the next step in your data architecture rebuild? Here's how to get started:

Watch the full webinar recording for more detailed technical insights and a demonstration.
Try Timescale Cloud: Our fully-managed service eliminates operational overhead so you can focus on your application. Sign up for a free trial.
Explore Open Source: TimescaleDB is open source and can be installed on your infrastructure. Installation guides are available for all major platforms.
Join the Community: Connect with thousands of developers working on similar challenges in our Slack community.

Learn More: Explore our documentation and tutorials for detailed guides on optimizing time-series workloads.

FAQ: Energy IoT and Time-Series Infrastructure

Q: Why are historian systems failing to scale with modern energy data?
A: They were designed for basic sensor logging, not high-ingestion analytics or flexible querying.

Q: Can modern databases handle out-of-order or late sensor data?
A: Yes. PostgreSQL-based systems allow updates and re-ingestion without breaking downstream logic.

Q: What’s a realistic compression rate for high-volume IoT data?
A: 90–95% compression is achievable without discarding any data.

Q: How do EVs and batteries change data needs?
A: They introduce high-frequency, dynamic streams that require continuous monitoring and historical visibility.

IoT Energy Data at Scale: Engineering Solutions Beyond Legacy Historians