5 ways AI is being used in the Energy industry

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Use cases of how five companies are using AI to improve their energy needs


1. DeepMind: DeepMind has trained an artificial intelligence system how to predict the energy output of Google wind farms in the U.S. The variable nature of wind makes it difficult to accurately predict how much energy a wind farm could produce in any given time period. However, DeepMind’ AI system, a neural network trained on widely available weather forecasts and historical turbine data, can predict wind power output 36 hours ahead of the actual generation with a reasonable degree of accuracy.

2. Seimens: Autonomous smart grid: Smart grid that contains many adjustable components already play an essential role in the transition to renewable energy sources. Software from Seimens can operate these grids autonomously with maximum efficiency. Also, deep neural network based reinforcement learning methods are emerging and gain popularity for smart grid application.

3. SparkCognition: SparkCognition is an Austin AI startup. SparkCognition leverages excellent deep learning and machine learning techniques to provide predictive maintenance capabilities. SparkCognition’s AI platform learns from data to understand operational states and failure modes of assets and uses this intelligence to warn of impending asset failures.

4. Google Sunroof: Google Sunroof project uses deep learning and machine learning to calculate how much sunlight your rooftop receives and how many solar panels you can have on your rooftop. In this way, users can save lots of money.

5. Chevron: Chevron led an effort to employ deep neural networks and fuzzy kriging (a numerical method applied to imperfect data) to analyze the viability of a reservoir in California’s San Joaquin Valley in 2016. This site proved a good candidate for the experimental approach because of the complexity of the geological features.

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