In the industrial world, and specifically the energy sector, the amount of connected devices, sensors and machines is continuously growing, resulting in the internet of energy, or IoE. IoE can be broadly defined as the upgrading and automating of electricity infrastructures, making energy production more clean and efficient, and putting more power in the hands of the consumer.
Given the vast amount of data the energy sector generates and the increasing number of sensors added, it is the perfect environment for machine learning applications. Artificial intelligence (AI) excels at finding subtle patterns in data sets of all shapes and sizes, particularly under complex or changing conditions.
Although data within IoE is growing at exponential rates, much of that data is traditionally siloed across business units (generation, transmission and distribution, energy trading and risk management, and cybersecurity). Extracting the wealth of data out of each of the silos and putting that data to work is needed to promote a better IoE experience and receive the benefits out of machine learning. Artificial intelligence capabilities can be incorporated to gain insight from all the data uniformly, allowing business units to transform into a collaborative system.
Generation: Prescriptive maintenance of turbines
Generation, the first major silo in the energy sector, is largely dependent on the work of turbines. Turbines consist of thousands of moving parts, and even the smallest disturbance can create major problems, causing unscheduled downtime, loss of power, safety concerns and other issues.
Applying AI and machine learning techniques to prevent unplanned downtime and catastrophic breakdowns is revolutionizing how utility companies operate. A standard approach of subject matter experts (SMEs) developing static, first-principle models places a tremendous burden on organizations to maintain and update them. Furthermore, the static nature of traditional models means operators are only able to view steady state operation of turbines, whereas the meaningful data is transient events like startups and coastdowns.
Transient conditions are where critical issues first materialize, but they are challenging to monitor because they occur over indeterminate lengths of time. Where a static model-based system is unable to solve this issue, an AI-based technology can. An artificial intelligence approach can start analyzing data and providing insights on day one, and continue to improve upon its own accuracy and effectiveness by learning from SME input.
Transmission and distribution: More than just smart meters
For the second silo of transmission and distribution, AI is able to tackle much larger problems. While smart meters and end user control of home appliances have generated excitement, they are not the most challenging big data problems being solved by machine learning.
Three specific areas in transmission and distribution where AI is playing a key role are:
- Energy disaggregation
- Power voltage instability monitoring
- Grid maintenance
In these areas, the collection, ingestion and action upon the data have created efficiencies in expenses and operations for companies using machine learning and AI technologies, as well as for their customers.
Energy disaggregation requires the utilization of machine learning because thousands of energy “signatures” must be analyzed to find patterns of usage. An analysis of energy signatures can predict suspicious consumption values, for example, due to physically or digitally manipulated devices, sophisticated thefts or meter malfunctions.
The second area, power voltage instability monitoring, faces an explosion of dynamic data surrounding minute instabilities in which human analysis falls short. Researchers can utilize machine learning techniques to identify voltage instabilities, thus preventing brownouts and blackouts on the grid.
The last area of transmission and distribution where AI is playing a key role is grid maintenance. While many companies are still struggling to use the data they are collecting, a machine learning algorithm can use the data or features to classify and ultimately predict failures well in advance. Because machine learning algorithms can automatically break features down into additional data and analyze them at machine speed, previously unseen correlations in the data are leading to new discoveries.
Cybersecurity: The modern battleground
The third major data silo in utilities is cybersecurity. The recent and continuous onset of attacks to critical infrastructure makes the need for new cybersecurity methods vital. An AI offering can identify, categorize and remediate a variety of threats including loss of personally identifiable information, zero-day malware and advanced persistent threat attacks.
To a mathematical algorithm, there is little difference between the aforementioned data and cybersecurity data. All input, regardless of source (a vibration sensor or a firewall log, for example), is simply a piece of information with unique patterns to an algorithm.
To combat the cyber front of industrial threats, an artificial intelligence product can automate the threat research process, prioritize threats based on confidence and display corroborating evidence to the analyst, significantly reducing both time to threat remediation and overall risk.
Energy trading and risk management
Energy trading and risk management is the final data silo in the energy sector. In the highly competitive and regulated utility business, there is a clear link between the company’s bottom line and forecast accuracy and reliability. If new techniques can provide more accurate forecasting, utilities can begin to offer better pricing to their customers.
AI techniques are providing insight into this process. With thousands of features from hundreds of sources, there are infinite ways to combine and correlate information. Looking for subtle, transient movements of price data on an hourly or even a second-by-second basis with millions of combinations is where AI excels.
Because utility companies need to buy oil, gas, coal, nuclear fuel and electricity, they are constantly at the mercy of volatile commodity prices. For this reason, utilities are using AI techniques to develop methodologies for market and credit risk aggregation.
With improvements in the sharing of data from data silos, the utilities industry can reap the wealth of new knowledge. From prescriptive maintenance to energy trading to cybersecurity, analytics will play an important role in how energy is produced and provided to consumers long into the future. As adoption increases, AI technologies will continue to learn and adapt, providing more value in the internet of energy.
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