Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Artificial Intelligence Is Crucial For The Energy Industry

May 17, 2017 | 681 Views

As the world begins to turn away from fossil fuels and depend increasingly on renewable resources, the energy sector is presented with a problem. Renewables are simply not as reliable as oil and gas, as they are largely dependent on weather conditions such as sunny skies and windy days. In a world where we become fully dependent on renewables, there is concern that supply may not always be able to meet demand.

This supply problem is compounded with the complications of individuals, businesses, and municipalities becoming small-scale energy producers themselves by way of solar panels and individual storage units connected to the grid. These producer-consumers, having varying and unpredictable patterns of individual production and consumption create instability on shared grids.

Producer-consumers cannot safely connect to a central, nationwide grid until we have predictive software able to understand and manage localized energy flows. The energy industry needs a smart technology that can ensure that there is an equilibrium between supply and demand at all times.

Enter artificial intelligence. Though the necessary technology is still in development, AI would be able to use predictive algorithms to balance grids, negotiate joint actions to self-heal networks in case of bugs or hacks, and to assess the reliability of production and consumption figures created by producer-consumers. The system will have to learn the minutiae of each locality's behavior of supply and consumption, with the ability to store or release energy as needed to keep the grid balanced.

Currently, there is no industry standard for the integration of producer-consumers' energy storage into the greater grid, creating a massive opportunity for innovation that will have global consequences. Many areas employ several different strategies to fill the gaps in renewal energy supplies. For example, for brief gaps in generation, the UK's National Grid turns to conventional power stations. For longer gaps, some power stations are kept on standby, a costly strategy that continues contributing the carbon emissions that the country is trying to combat.

Many research groups are already working on the necessary AI programs to solve these problems, and a number of tech start-ups have already begun developing such systems. Relatively simple forms of machine learning are beginning to be utilized.

Artificial intelligence doesn't just stand to help spread the use of renewable resources--it also holds profound potential for traditional energies. The oil and natural gas industries also have their eye on AI.

Texas-based oil and gas company Pioneer Natural Resources has said that using AI could help ensure accurate and optimal drilling locales. Like many other drilling companies, Pioneer has already been using predictive analytics, but they are yet to venture into AI. However, the transition could be coming very soon. Oil and gas companies are dedicating large research teams to the development of AI as for its potential to increase production without the need to hire many more workers, an attractive prospect as crude oil prices continue to be unstable.

Oil giant BP has openly talked about their investment in AI's potential to transform the field. Dan Walker, BP's Group Technology leader, has said that BP will be use the technology to combine datasets (flow rates, pressures, equipment vibration, etc.) with data from the natural environment (seismic information, ocean wave height) to transform the way they run and optimize their drilling operations.

BP has also begun testing utilizing AI technology on a smaller scale with new "personality pumps," in Chicago in New York, aiming to provide a more interactive experience at the pumping station. Customers can interact with an artificial intelligence personality called "Miles" which offers customers options such as trivia, music, and creating video e-cards that they can send to friends.

While the necessary systems needed to transform the renewable and traditional energy industries are still in development, we can expect big changes soon. When a company as large as BP proclaims, "AI is enabling the fourth industrial revolution," the world listens. Read More

Source: Oilprice
Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

Full Bio 
Follow on

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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