No, not THOSE storage problems. I mean energy storage problems.
Energy storage technology plays a fundamental role especially in the renewable energy sector, which is subject to strong production fluctuations.
Basically, we are talking about intelligent storage units powered by AI and adjustable according to the supply flow.
Artificial intelligence and especially machine learning, by creating models based on previously collected data, can help manage these flows and store excess energy to avoid grid overloads.
4. AI to support demand management
Improving energy production and distribution is certainly cool. But… What if we could reduce energy consumption?
Actually, demand management is already seeing an increasing implementation of AI-powered tools to improve energy management systems and overall energy efficiency.
For example, we can combine energy flow control systems with large industrial equipment, such as air conditioning units and furnaces, to automatically shut them down when power is low.
Another case is that of Google and its AI-focused company DeepMind, which managed to reduce the energy consumption of data centers by 15%.
Google declared it has already saved 40% of the energy consumed for cooling thanks to AI.
5. AI to enhance maintenance tasks
AI has proven to be a talented clairvoyant, an outstanding organizer… and the utilities’ best mechanic!
In fact, AI can help utilities with maintenance processes by replacing time-intensive and risky manual inspections, also via drones and robotics technology.
Recent developments in sensors and signal processing systems, combined with machine learning algorithms, offer new opportunities for efficient and rapid condition monitoring (CM).
CM examines the various activities of power facilities and grids, collecting data by specific sensors and processing it through a wide range of ML-based methodologies to detect malfunctions.
In particular, neural networks (NN), support vector machines (SVM), and decision trees are the most commonly used.
Detecting faults with algorithms
That’s the kind of approach chosen by San Diego Gas & Electric, an important utility that serves 20 million Americans.
This company was facing a massive problem of energy leakage and consequent revenue losses.
Instead of sending technical personnel every five minutes, they decided to implement AI and machine learning to analyze datasets from their grids and detect any possible fault.
Did it work? Well, considering that I’m writing about it, yes, it worked.
The company quickly resolved outage issues, customers were satisfied, the brand value was maintained, and the CEO’s daughter married Prince Charming. Happy ending!
6. AI to transform the customer experience
I know, the list is already quite long, but let me spend a few words on the retail side of the energy market.
Artificial intelligence has the potential to (positively) transform the customer experience and improve user retention.
Utilities can process huge amounts of users’ data thanks to ML algorithms and get insights into their preferences, habits, and discomforts.
This precious information will be used to predict customers’ churn, develop personalized offers, and offer targeted rewards.
AI, call centers, and customer care
AI is also improving the user experience by revolutionizing call centers, which can exploit modern technologies to respond to consumer queries and provide immediate assistance.
“No, sir, that’s a restaurant receipt, not your electricity bill.”
That kind of assistance. For that kind of query.
Another AI-connected advantage for customers is the selection of suppliers. User data can be cross-referenced with that of electricity companies to identify the offers best suited to their needs.
Lumator performs exactly this task by using software developed at Carnegie Mellon University and taking care of the switch without supply interruptions.
What is wrong with AI?
Until now, artificial intelligence looks as cool and perfect as any applicant according to their CV. Yep, it IS cool, but with a couple of flaws.
As already mentioned when talking about smart grids, data is the real fuel of AI-powered systems. But data is a strong point until it is stolen. Data protection and security issues are some of the major weaknesses in the implementation of AI in the energy sector.
For example, in 2018 the German Federal Office for Security recorded an abrupt increase in cyber disruptions on critical infrastructures, including energy facilities, compared to the previous year.
That’s why cybersecurity is becoming increasingly important to protect highly networked power grids from hacker attacks.
On the other hand, AI can also fortify our defenses against cyberattacks, learning from large amounts of data and thus spotting deviations such as the presence of Trojans.
“In 2018 the German Federal Office for Security recorded an abrupt increase in cyber disruptions on critical infrastructures, including energy facilities, compared to the previous year.”
Problems regarding privacy and energy consumption
A second data-related weakness concerns the handling of private user information by energy companies, which seems to be one of the main obstacles to the acceptance of smart meters.
Apparently, for many people, their energy consumption data is even more sensitive than their Google search history. If you know what I mean…