Energy has been the driving force behind the creation of much of the modern world. Industrialization and digitization were made possible by a robust energy grid. Artificial intelligence offers new opportunities for this industry to advance. These systems have required intense collaboration between energy producers and distributors. The introduction of artificial intelligence has provided new opportunities for efficiency.
Data annotation is a critical element of this new marriage between AI and the energy sector. From demand modeling to distribution mapping, AI can help get energy where it is most needed. The algorithms require unique kinds of data inputs. The tools used to measure and monitor energy use are also diversifying. As a result, AI can be applied to nearly all aspects of the energy industry.
Data annotation in the energy sector
The energy sector can be broken down into a few more specific areas. At the most basic level, energy production is the domain of a portion of the industry. Today, governments and corporations worldwide have started to green their sources. This means that traditional sources like oil and gas are joined by renewable sources like solar, wind, and hydro energy. Data annotation in these sectors will monitor energy production and emissions.
After energy is gathered, it also must be distributed. There are several ways in which AI can make this process more efficient. For example, energy transmission through power lines causes some energy to dissipate as heat. By some estimates, the amount of energy that is lost during the transmission process can compare to the total amount of energy used in a given year. Artificial intelligence and better processes for transmission (like superconducting materials) reduce this waste.
There are also huge opportunities to improve how that energy is used once it reaches your home. The increased awareness around climate change has implored energy companies to think about how to reduce energy use. Artificial intelligence presents a powerful way to model where energy is most needed. Appliances have also started using machine learning to improve their energy efficiency. If deeply incorporated into their process, this could reduce the amount of energy that is used overall.
Lastly, batteries can make use of artificial intelligence to improve their efficiency. The energy that is not immediately transmitted needs to be stored somehow. Engineers have been using these tools to better design batteries to preserve more energy. Training data is essential here because it can set the benchmark for what the expectations for these systems should be. This is one area with significant room for advancement in the coming years.
What are some examples of video and image annotation in the energy sector?
Focusing on video and image annotation, the energy sector is already using many of these tools. They can be applied in each of the energy production and distribution phases outlined above. In nearly every case, they aim to reduce some form of inefficiency. Some of the most prevalent examples of this include:
- Satellite imagery in the oil and gas industry
- Grid management
- Video monitoring of rigs and turbines
- Weather forecasting
Satellite imagery is generally one of the most promising datasets for the energy industry. Oil and gas exploration is often a time and energy-consuming process. Using satellite images from potential oil and gas sites can simplify this a lot. Data annotation with satellite images of oil and gas deposits to make predictions about where they would be located. Geological features have been shown to correlate to the presence of these materials. Algorithms can be used to identify these features and guide prospecting missions.
Grid management is another area in which video and image data analysis can improve the energy grid's efficiency. This could take many different forms. Real-time satellite data in cities, for example, could use light usage at night to model when and where energy is being used. Energy companies could plan where energy is sent while minimizing unnecessary uses elsewhere. Again, transmission is often costly. Being able to minimize unnecessary transmission could have major benefits for the industry.
The maintenance of machinery can also be improved with the use of video annotation. For example, oil rigs, wind turbines, and other production machines may lose efficiency as they are used. Knowing when to shut them down for regular upkeep is an important part of this. Video annotation can continuously monitor these machines to ensure their proper function. Predictive maintenance is one of the most promising fields across many industrial processes. The input data could include videos, sound data, and energy use data, working together to paint a full picture.
Weather forecasting is another important use of video data. Again, this will likely come from satellite data. If energy companies are aware of the weather conditions over a geographic area, they can model what energy use will be. Hot days require more air conditioning. Cold days need heat. Storms might knock out the grid. A properly annotated data set on how weather impacts energy use can improve how these energy companies model what usage might be.
The future of the energy industry
Oil and gas will likely be the most significant player in the energy industry for the next few decades. Although this has been detrimental to the environment in the past, creating tools that can improve how energy is used will be necessary. These tools can also be applied to the renewable energy grid once that becomes the standard. Implementing these AI/ML-driven processes now will help smoothen that transition.
Considering how a renewable grid fueling an automated world might transform things is interesting. For example, AI-driven factories using renewable sources could transform industrial processes completely. This is likely the future that we are heading towards. With the right kinds of data annotation, we can expect a total transformation in how we think about the collection and use of energy.