One Technology That Makes Renewable Energy More Efficient

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This article was originally published in The New Stack and is reposted here with permission.

Time series data can provide insight into ways to make energy production and consumption more cost-effective and efficient.

The year 2022 saw the impact that world events can have on global energy markets. The most drastic fluctuations affected fossil fuels, which led to greater discussion about the practicalities of renewable energy. Fortunately, the move toward increasing reliance on renewable energy remains a consistent trend.

According to the Center for Climate and Energy Solutions, in the United States, renewable energy is the fastest-growing energy source.

  • In the period from 2000 to 2020, renewables are up 90%, including a 42% increase from 2010 to 2020.
  • In 2020, approximately 20% of energy generation came from renewable sources. Analysts expect that share to rise to 35% by 2030.
  • In 2020, approximately 5% of consumed energy came from renewable sources.
  • Recent estimates predict the rate of renewable energy consumption to increase at an annual average rate of 2.4% over the next 30 years.

It’s not just the U.S. making strides in the realm of renewable energy. In 2020, 29% of global energy generation came from renewables. That same year saw a record amount of renewable power capacity — 256GW — across the world.

What does all this have to do with technology? While traditional fossil energy sources continue to be temperamental, there is some serious momentum behind renewables. The shift to renewables, however, means that much of the established infrastructure used in fossil fuel production and distribution no longer applies. Renewables require new infrastructures to track new system requirements and output. Technology plays a critical role in these new systems.

Time series data and energy

As companies seek to make energy production and consumption more cost-effective and efficient, they need insight into how these systems function. For instance, operators in this space want to track:

  • Overall energy generation levels to determine whether they can meet supply and demand.
  • Energy generation capacity for each device and the factors that influence device performance, such as device state, necessary maintenance for optimal performance and the impact of current conditions.
  • Reasons for service degradation, which might rely on previously collected data or data that still needs to be collected.

Time series data can provide insight into these areas and more. Any device or system that an operator controls can generate time-series data. This gives operators deep observability into systems, which enables them to make better business decisions and to arrive at those decisions faster.

Dealing with nature as an energy source is tricky. Energy sources, such as wind, hydro and solar, are not constant and require operators to adjust equipment regularly to accommodate environmental changes.

Renewable energy operators often face other challenges, too, which may include:

  • Device location – Devices that generate energy are often in remote areas. Individual devices within a larger system may also be geographically dispersed. Operators prefer to send out technicians only when devices require maintenance. Connectivity issues may also hamper data collection data from remote devices.
  • Data – Operators need to collect a lot of data from each device. Metric data intervals and data granularity may differ from one sensor to the next. Furthermore, they need long-term storage for all this data to enable historical analysis or production forecasting.
  • Rudimentary tools – Manual data collection, like walking the field, remains common and gets recorded in spreadsheets or on paper. This can lead to input errors and doesn’t scale.
  • Equipment – Renewable energy equipment is expensive and fragile, so it’s critical to protect assets to maximize usage and performance.

Open source technologies, like the InfluxDB time series database, can help operators solve or mitigate these issues, driving innovation and allowing developers to bring ideas to fruition faster.

Time series is making a difference in renewable energy

The term “renewable energy” encompasses a broad swath of energy production, storage and consumption processes. The following examples demonstrate some of the ways that businesses around the world use time series data to drive innovation and improvement in and around renewable energy.


Bboxx develops and manufactures products to provide affordable, clean solar energy to off-grid communities in the developing world. It provides customers with solar panels connected to a battery and a set of USB and DC connectors to power lights, radios and other low-powered appliances. Over 3.5 million people across 35 countries have access to electricity through Bboxx. Schoolchildren can study under a clean light source, rather than burning kerosene and inhaling soot and fumes.

InfluxDB is a core part of the Bboxx solution and collects data relevant to remote monitoring, billing and alerting for distributed devices. With InfluxDB, Bboxx can gain insight from its data and apply lessons learned from analyzing historical data to develop new and exciting products that exceed customer expectations.

Example of alert powered by InfluxDB

Example of alert powered by InfluxDB


EnerKey is a Finnish company that operates a platform to drive sustainability and energy management for indoor spaces by analyzing consumption data. It helps organizations reduce their environmental footprint by combining weather, time series and geospatial data to monitor energy consumption, indoor air quality, waste and emissions. Having access to this data in real time enables EnerKey users to detect energy usage fluctuations and to make informed decisions about how to improve efficiency.

EnerKey uses InfluxDB to store and manage billions of metrics and to perform analysis on historical data. The company also uses InfluxDB to combine time series data with other data types, like weather data, to better understand how external climate factors affect indoor energy usage. It can also use these combined data sets to fuel predictions about future energy consumption. These energy efficiencies turn into cost savings for EnerKey users, and the reduction in energy usage is a win for the environment.

Data Acquisition Architecture

Data Acquisition Architecture

Graphite Energy

Graphite Energy is an Australian company focused on industrial decarbonization. It builds Thermal Energy Storage (TES) systems that decouple variable, intermittent and low-cost renewable energy sources, such as wind farms or solar photovoltaic fields, from the process requirements of manufacturing plants to deliver reliable, predictable heat for industrial applications.

The company uses InfluxDB throughout its solution architecture, both at the edge and in the cloud, to collect data on TES machines in the field. Each machine produces approximately 100 data series that get recorded anywhere from every 1 to 15 seconds for a total of around 1,000,000 records per day. Graphite Energy uses this data to create real-time digital twins of its field devices. These digital twins let Graphite Energy roll forward and backward in time to track device performance and are becoming a very powerful part of its predictive toolkit for production optimization.

Graphite Energy time series data architecture

Graphite Energy time series data architecture

Whether we’re talking about companies involved with energy generation or energy consumption, and whether that’s at the consumer or industrial level, time series is mission-critical data in modern applications. Having a best-in-breed tool like InfluxDB to collect, store, manage and analyze that time series data in real-time provides observability and insight into these systems that are changing the global energy paradigm and creating a more sustainable energy future for everyone.