How Artificial Intelligence is Powering the Energy Industry

In the energy industry, AI is becoming more and more important, there is a very high potential for the future design of energy systems and to increase the horizons for sustainable energy development. In every area like electricity trading, smart grids, or the sector coupling of electricity, heat, and transportation.

To establish an interaction between the smart grids, smart meters, and IoT devices and then using AI to solve all the energy-related problems is the objective here. AI can help improve power management, efficiency, and transparency and increase the use of renewable energy sources.

The worldwide energy sector is facing challenges related to increasing demand, efficiency, changing supply and demand patterns. The studies reveal that these challenges are being efficiently met in the developed nations but there is a huge scope of implementation of AI in developing nations. The reason being — the energy industry is still a traditional industry; digitalization of the energy sector is a prerequisite for an increase in the use of AI.

Data volumes need to be built to make the energy industry more efficient and secure by analyzing those data and adding a layer of intelligence.

Watch how AI is a game changer in the Energy Industry:

Energy is the Booming Sector of the Era

Talking about the energy sector, there was a very powerful blog that came to my mind. It was written by the former richest man on the planet — Bill Gates. He wrote an essay online at “The blog of Bill Gates” that was dedicated to all the college graduates of the year 2017.

In that blog he mentioned ““If I were starting out today… I would consider three fields. One is artificial intelligence (AI). We have only begun to tap into all the ways it will make people’s lives more productive and creative. The second is energy, because making it clean, affordable, and reliable will be essential for fighting poverty and climate change.” The third field he mentioned was biosciences.

AI in energy is bound to improve living conditions today and for future generations. It is in every sense enabling the fourth industrial revolution because it has a tremendous potential to help in delivering the next level of performance. It’s poised to revolutionize the way we produce, transmit and consume energy.

How AI is Transforming Energy Industry

Intelligent Energy Storage

Storing energy that’s produced efficiently is the most crucial task at hand. AI can improve energy storage technology by making it simple to integrate various technologies including renewable-powered microgrids, utility-scale battery storage, pumped hydro, and more.

To store energy in modern grids is the most important function. The power sources like wind, water, and solar put a strain on power brokers to balance supply and demand. Storage cost can be optimized and hence we can supply more energy at reasonable rates to the regions that need them the most.

AI plays an important role in the grid’s ancillary services. These functions help grid operators balance and support the transmission of energy from generators to the end customers.

Demand and supply work hand in hand and using intelligent allocation can save the otherwise wasted power can be used in a better manner and that’s getting bang for the buck. AI also drastically improves the frequency and voltage control caused by intermittent power generation.

Younicos, a berlin-based energy storage firm has been into developing intelligent storage technologies that have saved energy from being wasted.

Autonomous Robots for Energy Industry

Energy production is a risk-oriented job, many laborers lose their life while working at an energy power plant. The most advanced robotics today is used in the energy sector. They are extremely helpful in conducting inspections of solar, wind, and hydro farms, cleaning the turbine blades, de-icing turbine blades.

Besides this drones are used at energy power plants. These drones carry high-resolution, infrared, thermal cameras which can be used to collect pictures and videos of solar and wind farms. In traditional scenarios, the operators were expected to hang around with the help of ropes and carry out wind turbine inspections. This was a very risky task and safety issues emerged all the time.

The robots today also carry out solar panel inspections, clean hydro and nuclear power plants, identify the predictive repairs and do the needful installations.

Data Annotation to Train Robots

Algorithms, data, and processing are the indispensable elements of AI. Every artificial intelligence project starts and progresses with data. In today’s scenario, it is very convenient to train artificial intelligence with big data.

Data annotation plays a critical role in enabling robots to understand functions just like humans. Using the technique of data annotation robots can identify, map, and complete the task that is assigned to them with utmost perfection.

There are three common types of data annotation in the field of energy robots:

• 2D Bounding Box
• Polygon
• Segmentation

Labellerr provides a data annotation service for AI-powered applications in robotics. Their machine learning-powered data annotation platform helps retailers improve the accuracy of their computer vision models in the most cost-effective way.

Benefits of Labellerr’s Data Annotation Platform:

• Label data at 10x speed using Labellerr’s ‘Auto Labeling’ feature
• Track work quality and worker productivity with a personalized dashboard experience.
• Get relieved from the hassle of reviewing each dataset, instead review only the ones having low confidence scores.

To build best-in-class energy robots, adopt the smartest and the fastest data annotation platform — Labellerr.

Intelligent Power Consumption

All the gadgets that consume power today in the developed nations have electrical smart meters, they provide in-depth information about energy consumption and enable informed self-regulation of energy usage.

The latest in trend is the new ai-fueled smart meters, these are going to be a very important part of the smart home solutions. They are not widespread as of now but have tremendous potential. With the help of IoT, every device can communicate with the other and all of these devices can be synced with the smart energy-efficient home solution.

These devices can function in a smart way and reduce energy wastage like controlling the air conditioner dynamically, advising the charging of electric cars during hours with lower electric costs, controlling lighting, and managing the functioning of household appliances.

Smart Trading of Electricity

You must have heard that Bhutan supplies electricity to India, Nepal, and China. Yes, electricity is just like any other commodity that can be bought and sold with ease and even traded in open markets.

To make these trades beneficial and sustainable there are multiple factors to be taken into consideration. The data regarding weather, grid demand, or supply balance should be monitored and the patterns must be analyzed by the sellers and buyers alike.

Those traders who can best understand the data are bound to have a competitive advantage in the marketplace. DeepMind from IBM began applying artificial intelligence algorithms to 700MW of google’s wind power capacity in the U.S. By utilizing the technique of deep learning (neural networks) with historic data related to weather, historical turbine data the machine could accurately predict the power output 36 hours in advance.

This system increased the value of their wind energy by 20% in less than a year.

Data Annotation in Energy Industry — Most Popular Application

The world is moving towards adopting a system to automate electricity data annotation leveraging cheap wireless sensor nodes. Characteristic sensory stimuli captured by sensor nodes placed next to appliances are translated into appliance operating state and correlated to electricity data, autonomously generating the annotation of electricity data with appliance activity.

Because of the low-power wireless networking, we are now able to gather the data on electricity usage in near-real-time remotely. This provides great insights on the amount of energy consumption happening each second and the pattern can be determined and accordingly the supply and distribution of energy can be planned.