Artificial Intelligence, or AI, has gained relevance in many different fields of our life, using machine learning to analyse historical and new data in order to make predictions, improve control operation and perform tasks much faster than a human and with more efficiency.
The energy sector is using AI to increase energy efficiency by reducing consumption, improving energy storage and grid stability, making predictions about energy consumption, to have more accuracy to find oil & gas and many other applications.
When it comes to renewable sources, AI is improving the weather forecasts for the development of new plants and to make better planning of control & maintenance. Let’s take a look in some projects and how they are affecting the energy sector.
It is impossible to talk about AI without mentioning Google, the company that provides us with the first Big Data project in the world. DeepMind was a startup acquired by Google and this is how it defines itself:
“Artificial intelligence could be one of humanity’s most useful inventions. We research and build safe AI systems that learn how to solve problems and advance scientific discovery for all”
DeepMind started in 2010 mixing concepts of mathematics, computer science, machine learning, neuroscience, engineering and simulation. The project first applied the concepts in computer games and the most famous is AlphaGo program, which was the first to beat a professional player of Go (if you didn’t watch the movie, I really recommend it).
One of the greatest achievements of the project was related to energy, back in 2016:
DeepMind AI Reduces Google Data Centre Cooling Bill by 40%
Using historical data collected in a data center to train an ensemble of deep neural networks, the energy efficiency was improved, the CO2 emissions were reduced and the bill was reduced by 40%.
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4. Machine Learning System Design
SunShot is from US Energy Department, and the aim is “to reduce the total costs of solar energy by 75 percent, making it cost competitive at large scale with other forms of energy without subsidies by the end of the decade”
The SunShot Initiative’s Solar Forecasting was launched in 2012 in a partnership with IBM to use big data and machine learning to improve the forecasts for the renewable energy sector.
In 2017 the program own the Utility Variable-Generation Integration Group’s 2017 Annual Achievement Awards, which honor energy professionals for their success in reliably and economically integrating renewable energy.
Did you ever heard the term IoT (Internet of Things)? This is a concept referred to the interconnection of electrical equipment with the internet, transmitting data and providing the user to remotely control the objects, improving efficiency of energy consumption.
Verdigris Technologies mix IoT with AI to provide information about the energy consumption in large commercial buildings through the installation of Wi-Fi devices to track the energy. With the visualization of all energy consumption of the building, the operator can identify where are the most energy consumption and to identify where are failures in the electrical system.
Nnergix provides weather analytics services with a 240 hours ahead of forecasting prediction. The company combines satellite data with machine learning algorithms to predict the weather of a specific region.
In the meteorology part, the company uses the “Sentinel Weather” to correlate weather and site operation.
“Sentinel uses weather forecasting data from trusted resources like NREL, NOAA and ERCF and provides an interface in which personalized alerts can be configured so that notifications are received when an extreme event could endanger your facilities.”
One of the biggest challenges in climate change is energy storage. Renewable sources are intermittent, in other words, the electricity generation is not constant, it depends on environmental factors and when the wind is blowing and sun is shining there might be excess of electricity, so the storage of it is essential to explore more the potential of these sources.
Athena is a Stem’s technology, using raw data from solar panels, price signals and weather forecast to improve the operation of the battery. As a true machine learning, with more data it works more precisely.
Credit: BecomingHuman By: Afonso Lugo
