The energy industry is changing fast—renewables, batteries, EVs, and microgrids are flooding systems with high-frequency, high-volume data. Legacy “historian” databases can’t keep up.
In this post (based on our IoT + Energy webinar), we’ll break down the 10 biggest data challenges facing energy systems—and how developers are solving them with modern time-series infrastructure built on PostgreSQL and Timescale
1. Renewables Have Fundamentally Changed Grid Operations
The Challenge: Unlike traditional power generation where output can be increased on demand, renewable sources like wind and solar are inherently intermittent. Wind turbines only generate when the wind blows, and solar panels only produce when the sun shines—regardless of when consumers need electricity.
The Data Impact: This has transformed grid operations from simple supply-demand balancing to a complex orchestration requiring sophisticated predictive analytics, real-time monitoring, and intelligent storage management—all driven by time-series data.
"Renewables have transformed generation. This is described as the biggest change since the shift from AC to DC power. The grid was easy to balance before, but renewables are a much different ballgame."
2. Energy Innovation Outpaces Traditional Data Systems
The Challenge: The energy sector is seeing rapid innovation, but many companies still rely on legacy "historian" systems that weren't designed for the volume, velocity, and analytics requirements of modern energy applications.
The Data Impact: Energy innovators are increasingly adopting modern approaches—either next-generation historians or custom-built data stacks combining message buses, time-series databases, and visualization platforms.
"People in energy are more likely to be thinking outside the box. They're more likely to be changing up things they were doing in the past and trying new things. They're more likely to be capturing a large amount of data and trying to analyze it in a different way."
3. Energy Startups Lead in Data-Driven Business Models
The Challenge: Energy innovation isn't just about technology—it's about building entirely new business models around data insights.
The Data Impact: From battery optimization services to microgrid management platforms, innovative companies are building their core value proposition around analyzing energy data and delivering actionable insights.
"When we see a lot of innovation in energy, the underlying thing we see are people saying, 'There's a lot of data here. I can do something with that data. I can analyze that data and make a product out of it."
4. Batteries Require Sophisticated Time-Series Analytics
The Challenge: Batteries—whether in EVs, home systems, or grid installations—require constant monitoring of multiple parameters (voltage, current, temperature, charge cycles) to optimize performance and predict lifespan.
The Data Impact: Battery management generates high-frequency, multi-dimensional time-series data that must be stored efficiently while remaining accessible for both real-time operations and long-term analytics.
"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 who have battery use cases."
5. Electric Vehicles Are Reshaping Demand Patterns
The Challenge: EVs are fundamentally altering electricity consumption patterns, creating more overnight charging demand and flattening the traditional demand curve.
The Data Impact: Utilities and grid operators need to analyze these changing patterns to optimize infrastructure investments, adjust rate structures, and ensure grid stability—all requiring sophisticated time-series analytics.
"EVs have changed demand. Previously if you were to look at a power grid, you'd see fluctuation at the times when people would be using air conditioning or when people would be up doing things. That's really changed with electric vehicles."
6. Data Compression Without Loss Is Critical
The Challenge: Energy applications generate massive volumes of time-series data that would be prohibitively expensive to store in raw form for years.
The Data Impact: While traditional historians typically compress by downsampling (losing data points), modern time-series databases like TimescaleDB can achieve 90%+ compression while preserving full data fidelity.
"This is a bit of a hot take, but I think you need a database that's going to let you compress your data without loss. In the world of historians, compression usually means downsampling, so it means compressing the data by throwing away data points you might not care about. I would argue that you need a database that's going to let you compress your data but not lose anything."
Here’s how you can enable native, lossless compression on a hypertable in Timescale:
-- Enable compression on the hypertable
ALTER TABLE readings SET (
timescaledb.compress,
timescaledb.compress_orderby = 'time DESC'
);
-- Compress chunks older than 7 days
SELECT compress_chunk(i)
FROM show_chunks('readings') i
WHERE i < now() - interval '7 days';
📦 How Timescale compresses data without losing fidelity
7. Late-Arriving Data Challenges Traditional Databases
The Challenge: IoT data isn't always delivered in perfect sequence. Devices may go offline temporarily and later send backlogged data, or readings might need correction after calibration.
The Data Impact: Energy data systems must handle late-arriving data and corrections without complex workarounds—a capability that many specialized time-series databases struggle with.
"You need to account for late arriving data and modifications. As much as we would like it to be, IoT data or energy data isn't immutable, sometimes things do change."
8. Energy Data Architecture Requires Both Time-Series and Relational Capabilities
The Challenge: Energy applications often need to combine time-series data (readings, measurements) with relational data (asset information, customer data, geographical details).
The Data Impact: Hybrid databases that combine time-series optimization with relational capabilities provide the flexibility needed for comprehensive energy analytics.
👉 Best practices for modeling time-series data
"If you want to ask complex questions of your database and sort of combine OT and IT data, then you need a database that can handle real-time analytics queries alongside time series queries."
9. Real-Time Analytics Drive Operational Efficiency
The Challenge: Energy systems require both historical trend analysis and real-time operational intelligence to optimize performance.
The Data Impact: Continuous aggregates and materialized views can dramatically improve query performance for common analytics patterns, enabling real-time dashboards without sacrificing historical analysis capabilities.
In the webinar demo, the original query ran in 17 milliseconds, which is pretty fast. But then if we use our continuous aggregate that we just made, then it runs in two milliseconds, which is a lot faster.
To make queries faster without reprocessing raw data, you can use a continuous aggregate. Here’s an example that calculates daily average temperatures per device:
CREATE MATERIALIZED VIEW daily_avg_temp
WITH (timescaledb.continuous) AS
SELECT
device_id,
time_bucket('1 day', time) AS day,
avg(temperature) AS avg_temp
FROM readings
GROUP BY device_id, day;
10. The Power of Modern Data Architecture
The Challenge: Octave Energy needed a robust data platform to support its innovative business model of giving EV batteries a second life as commercial storage systems.
The Data Impact: By switching from AWS Timestream to Timescale, Octave achieved 25x data compression, faster queries, and more sophisticated analytics capabilities—all critical for their battery performance prediction models.
"Timescale has proven to be a key enabler of Octave's data-driven battery cloud. They are a big user of our compression, our continuous aggregates, and they really love our ease of use."
Moving Forward with Your Energy Data Strategy
These insights highlight how energy innovators are leveraging modern time-series data management to drive the industry's transformation. 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?
Watch the full webinar recording for more detailed insights and a technical demonstration
Try Timescale Cloud with a free trial to experience high-performance time-series data management
[Join our Slack community](https://slack.timescale.com/) to connect with other developers working on energy data challenges
Explore our documentation for detailed guides on optimizing time-series workloads
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