Back in 2017, Bill Gates penned a poignant online essay to all graduating college students around the world whereby he tapped artificial intelligence (AI), clean energy, and biosciences as the three fields he would spend his energies on if he could start all over again and wanted to make a big impact in the world today.
It turns out that the Microsoft co-founder was right on the money.
Three years down the line and deep in the throes of the worst pandemic in modern history, AI and renewable energy have emerged as some of the biggest megatrends of our time. On the one hand, AI is powering the fourth industrial revolution and is increasingly being viewed as a key strategy for mastering some of the greatest challenges of our time, including climate change and pollution. On the other hand, there is a widespread recognition that carbon-free technologies like renewable energy will play a critical role in combating climate change.
Consequently, stocks in the AI, robotics, and automation sectors as well as clean energy ETFs have lately become hot property.
From utilities employing AI and machine learning to predict power fluctuations and cost optimization to companies using IoT sensors for early fault detection and wildfire powerline/gear monitoring, here are real-life cases of how AI has continued to power an energy revolution even during the pandemic.
Top uses of AI in the energy sector
#1. Innowatts: Energy monitoring and management The Covid-19 crisis has triggered an unprecedented decline in power consumption. Not only has overall consumption suffered, but there also have been significant shifts in power usage patterns, with sharp decreases by businesses and industries while domestic use has increased as more people work from home.
Houston, Texas-based Innowatts, is a startup that has developed an automated toolkit for energy monitoring and management. The companys eUtility platform ingests data from more than 34 million smart energy meters across 21 million customers, including major U.S. utility companies such as Arizona Public Service Electric, Portland General Electric, Avangrid, Gexa Energy, WGL, and Mega Energy. Innowatts says its machine learning algorithms can analyze the data to forecast several critical data points, including short- and long-term loads, variances, weather sensitivity, and more.
Related: The Real Reason The Oil Rally Has Fizzled Out
Innowatts estimates that without its machine learning models, utilities would have seen inaccuracies of 20% or more on their projections at the peak of the crisis, thus placing enormous strain on their operations and ultimately driving up costs for end-users.
#2. Google: Boosting the value of wind energy
A while back, we reported that proponents of nuclear energy were using the pandemic to highlight its strong points vis-a-vis the short-comings of renewable energy sources. To wit, wind and solar are the least predictable and consistent among the major power sources, while nuclear and natural gas boast the highest capacity factors.
Well, one tech giant has figured out how to employ AI to iron out those kinks.
Three years ago, Google announced that it had reached 100% renewable energy for its global operations, including its data centers and offices. Today, Google is the largest corporate buyer of renewable power, with commitments totaling 2.6 gigawatts (2,600 megawatts) of wind and solar energy.
In 2017, Google teamed up with IBM to search for a solution to the highly intermittent nature of wind power. Using IBMs DeepMind AI platform, Google deployed ML algorithms to 700 megawatts of wind power capacity in the central United States--enough to power a medium-sized city.
IBM says that by using a neural network trained on widely available weather forecasts and historical turbine data, DeepMind is now able to predict wind power output 36 hours ahead of actual generation. Consequently, this has boosted the value of Googles wind energy by roughly 20 percent.
A similar model can be used by other wind farm operators to make smarter, faster and more data-driven optimizations of their power output to better meet customer demand.
IBMs DeepMind uses trained neural networks to predict wind power output 36 hours ahead of actual generation
#3. Wildfire powerline and gear monitoring In June, Californias biggest utility, Pacific Gas & Electric, found itself in deep trouble. The company pleaded guilty for the tragic 2018 wildfire accident that left 84 people dead and PG&E saddled with hefty penalties of $13.5 billion as compensation to people who lost homes and businesses and another $2 billion fine by the California Public Utilities Commission for negligence.
It will be a long climb back to the top for the fallen giant after its stock crashed nearly 90% following the disaster despite the company emerging from bankruptcy in July.
Perhaps the loss of lives and livelihood could have been averted if PG&E had invested in some AI-powered early detection system.
Source: CNN Money
One such system is by a startup called VIA, based in Somerville, Massachusetts. VIA says it has developed a blockchain-based app that can predict when vulnerable power transmission gear such as transformers might be at risk in a disaster. VIAs app makes better use of energy data sources, including smart meters or equipment inspections. Related: Worlds Largest Oilfield Services Provider Sells U.S. Fracking Business
Another comparable product is by Korean firm Alchera which uses AI-based image recognition in combination with thermal and standard cameras to monitor power lines and substations in real time. The AI system is trained to watch the infrastructure for any abnormal events such as falling trees, smoke, fire, and even intruders.
Other than utilities, oil and gas producers have also been integrating AI into their operations. These include:
By Alex Kimani for Oilprice.com
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