Key Takeaways
- Energy data analytics has the power to change the ways we generate, transmit, and consume energy.
- Renewable energy sources increasingly rely on energy data analytics to improve their performances and balance power grids.
- Energy data analytics can offer greater energy efficiency, helping the world transition to clean energy and reduce climate-changing emissions.
- The technology is still relatively new, with much to discover, improve, and debate.
As its name suggests, energy data analytics refers to the process of analyzing big data from the energy industry. It aims to improve efficiency, give valuable performance insights, and help us in the energy transition to renewables.
Yet that narrow explanation only scratches the surface of energy data analytics.
That’s because the world’s energy systems are undergoing a digital revolution. Humans cannot keep up with the information available through these new processes of automation. We need help with the decision-making these analytics demands, and that also comes from digital sources, like artificial intelligence (AI).
Learn more about our emerging use of energy data analytics and its vital role in the battle against climate change emissions.
What Is Energy Data Analytics?
Energy data analytics refers to the analysis of big data from the energy industry. Underneath that phrase is the exciting part about energy data analytics.
Our energy systems are increasingly digitized, from smart meters to smart grids. Big data, unimaginable amounts of information, flows in real time between consumers, power plants, and those who operate power grids.
Wind turbines alone generate up to 400 billion data sets globally every year. Humans cannot meaningfully interpret such vast stores of real-time data, let alone react to it. Fortunately, machine learning and artificial intelligence can process big data analytics for the energy industry.
These powerful computing operations can analyze all energy consumption and production aspects in real time. Algorithms churn through datasets to give insights. That ability opens up a wide range of wonderful capabilities to the energy sector.
Why Do We Need Energy Data Analytics?
Traditional energy supply chains consisted of large, centralized power plants pushing vast quantities of power over enormous power networks. Excess energy could be lost, power demand surges could result in power outages, and inefficiency was the watchword.
Fast-forward to the 21st century, and decentralized energy generation is taking over. Massive power plants still exist, but the grid also receives electricity from renewable energy sources plus millions of micro-generators, like rooftop solar panels, called distributed energy resources (DERs). Smart technology means these inputs can send real-time data about their energy use or production.
That’s because people and industry need power even if the wind doesn’t blow and stops wind farms from producing electricity. Cloudy days reduce solar farm’s efficiency, but the grid must still supply power.
For example, drivers with EVs might switch from charging their cars to pushing energy back into the grid to balance it. With so many flickering data sources, balancing the power grid and understanding inefficiencies is almost impossible for a person.
That’s where energy data analytics steps in. Data management on cloud-based data platforms permits essential predictive forecasting of energy supply and demand.
And it’s not just utilities that can improve their performance. Studies suggest most companies could save 10% on costs through energy data analytics. People at home can use smart technology to analyze their energy use and reduce their energy bills.
What Are the Benefits of Energy Data Analytics?
There are many benefits of energy data analytics. They include:
- The chance to optimize our energy management and usage.
- Machine learning means real-time monitoring of energy supply and demand on power grids, with real-time decision-making easing surges or drops in energy.
- Insights into energy consumption trends during different weather patterns, long-term projections, and how climate change changes energy demands.
- Identify potential energy savings and efficiencies in the grid for energy providers.
- Track environmental goals such as renewable energy and sustainability targets.
- Predictive maintenance possibilities can be identified by spotting wear and tear, leaks, and other repair issues as they happen. This replaces previously inefficient maintenance during pre-defined periods — for example, replacing transmission poles every 10 years “just because that’s what we do.”
- Predictive analytics helps energy companies and utilities plan future energy production, avoiding blackouts and grid downtime.
- Machine learning can switch grids to save excess electricity production by pushing electricity into utility storage batteries, EV batteries, pumped hydro storage, or other energy stores.
Energy data analytics is really about the best management of precious resources. The benefits of data analysis include:
- Reducing costs for producers and customers
- Improving power production and profitability
- Making the most of renewable energy sources
- Tracking and improving the lifecycle of all parts of the energy production chain
Energy data analytics can also improve the user experience at home. For example, it can notify people via smart hubs that lights have been left on or that rooms are being heated or cooled needlessly. These can then be turned off remotely.
How Does Energy Data Analytics Work?
When considering the reach of energy data analytics, the list goes on: smart grids, smart appliances, solar farms, wind turbines, and electric vehicles. These all form part of a modern-day smart grid that energy data analytics relies on to work.
The software tracks all these players’ energy use or input in real-time, sending data to a central (often cloud-based) database. Data scientists use algorithms so machine learning and AI can process and interpret big data. This creates distribution management systems that help with the visualization of the grid’s real-time status, from outage risks to surges or maintenance issues.
Energy analytics tools present results, conclusions, and suggestions. Systems can be programmed to make decisions automatically or require human intervention to approve or modify their recommendations.
How Do Machine Learning and Artificial Intelligence Enhance Energy Data Analytics?
Machine learning and AI enhance energy data analytics primarily by forecasting supply and demand trends and optimizing energy use.
For example, on the supply side, they analyze weather patterns to predict electricity output at solar farms or from wind turbines. Simultaneously, they forecast how people and industry will react to those weather patterns. A stormy winter’s day is great for wind energy, which can power people’s homes. But that needs to be balanced with the drop in solar power during the storm, something energy data analytics works out. It can even calculate the probability of trees falling and downing lines.
Incredibly, some AI systems can “look” up to 36 hours ahead. Google’s DeepMind’s predictions have improved the efficiency of the company’s 700 megawatts (MW) renewable energy fleet by 20% thanks to energy data analytics.
These “learnings” increase grid security, lessen the likelihood of power blackouts and improve reliability. Optimizing the grid is vital as we transition to a renewable energy-driven energy system.
What Are Some Examples of Successful Energy Data Analytics Applications?
Whether you know it or not, energy data analytics is already part of our day-to-day lives.
Multinational utility company E.ON uses a machine learning algorithm and energy data analytics to predict when electricity transmission wires need replacing and how much power its wind farms will generate. Hepta Airborne feeds drone footage of a power network’s lines into a machine-learning platform to identify maintenance issues.
Smart homes can tell owners and energy providers what appliances use power and when and compare energy use to similar properties nearby. Such insights allow for energy efficiency learning and improvements.
California will need energy data analytics to successfully feed the stored power in millions of its EVs into the power grid during surges in demand. This balancing could happen during heat waves when people reach for the air conditioning to keep cool. Some think this mobile energy fleet could be the equivalent of two nuclear reactors coming online in the Golden State.
Firms like Kraken claim to have brought utility companies savings of up to 40% worth more than $300 million thanks to energy data analytics. Oracle foresees a world in which AI and machine learning can resolve customer inquiries about power, bills, energy use, and even the best energy plan for people without needing a human-to-human phone call.
The potential of energy data analytics will grow exponentially once more grids, homes, appliances, and data-friendly devices come online.
What Are the Key Challenges for Energy Data Analytics?
That said, energy data analytics is not a panacea for energy markets. Many parts of power grids are not sufficiently modernized to handle smart technology and real-time readings or they have incompatible systems. Investment in grid modernization is costly and another challenge to adoption.
Indeed, some utilities and companies are unwilling or unable to share sensitive data with competitors or neighbors. Smart communication is not seamless and encounters both regulatory (privacy) and industry pushback.
General questions arise about what data needs to be collected, when, by whom, and from whom, including how those data sets are stored and processed. Storage, protection, and data privacy all add levels of challenges.
The technology itself is on a learning curve. Some algorithms return errors, or there are errors in the code, or the returned data is not of sufficient quality. There is a global shortage of trained staff. Questions about people’s privacy—should utilities know how much you watch your smart TV?—swirl around.
Cloud-based systems are also vulnerable to cyberattacks. In 2021, a ransomware attack on the Colonial Pipeline in the U.S. closed down one of the country’s main oil pipelines, stealing 100 gigabytes of sensitive data.
Ironically, AI and machine learning require a lot of computing power, and making a more efficient grid will require a lot of energy.
How Can Energy Data Analytics Support the Transition to Renewable Energy Sources?
Experts predict the world will need to meet 85% of its electricity needs from renewables by 2050 to slow global warming. That’s a considerable transmission to what is currently an intermittent energy source.
Sporadic renewable energy sources need agile support systems to reach their maximum potential. Excess energy from wind turbines and solar farms must be saved in energy storage systems for later use rather than wasted. Energy data analytics can identify potential peaks and troughs in real time and switch renewables from transmitting power to saving it, a vital part of the energy transition.
Without the predictive ability of energy data analytics, a significant amount of this clean energy could go to waste. However, things are improving, with wastage rates globally down to around 3-4%. Renewables powerhouse China has reduced its renewables wastage from 16% in 2012 to 3% a decade later.
How Can Energy Consumers Benefit From Insights Derived Through Data Analytics?
Streamlined energy services offer several benefits to customers. More efficient utilities can detect issues with the network in or around properties or pass on savings through lower energy prices. However, if price drops seem unlikely, energy data analytics might still be helpful at home.
Smart meters and smart homes with smart applications can learn about your energy behaviors. People can switch off energy-guzzling appliances remotely, and some software can turn things like the heating or lighting off when no one is home. Analyzing routines could lead to machine learning offering ways to optimize energy use throughout a property.
Electric vehicle owners can charge their cars when electricity prices are low — for example, at night. They can return that energy to the grid during peak periods, earning money or bill credits for their participation, called vehicle-to-grid (V2G). People are also helping build a more resilient, agile, modern power network.
The primary benefits are energy efficiency and the potential to lower energy bills. A secondary effect is a reduction in energy-related emissions without sacrificing lifestyle choices.
Energy Data Analytics: Powering the Present and Future
Thanks to energy data analytics, enormous data sets related to energy use can now be analyzed. Utilities, companies, and homeowners can see how they use energy in real-time and where and how it is generated.
Machine learning and artificial intelligence (AI) boost computer processing power with algorithms that allow insights and identify trends that can optimize energy efficiency across the energy industry. But it’s not just for the big players. Energy data analytics has many positive effects on consumers, highlighting ways to save energy—and money—through energy efficiency at home.
More investment and use of energy data analytics push the boundaries of regulations, and they require a lot of power to run. They’ll become even more critical as the world transitions to intermittent renewable energy sources that require constant monitoring to avoid power blackouts.
Like the giant datasets it’s based upon, energy data analytics is complex and requires human decision-making to answer issues the algorithms uncover. The final reckoning will be whether the benefits of using big data to improve the power grid outweigh the costs.
Brought to you by amigoenergy
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