Energy in any form is an essential requirement, with the growing population and demands across the globe for resources that are already limited, need to be fulfilled in order to live a peaceful and war-free life.
The interaction between AI and energy is magical, AI can be seen as an important key to solving many of the problems we will face in the future.
“If I were starting out today and looking for the same kind of opportunity to make a big impact in the world, I would consider three fields. One is artificial intelligence. We have only begun to tap information all the ways it will make people’s lives more productive and creative. The second is energy because making it clean, affordable, and reliable will be essential for fighting poverty and climate change.” says Bill Gates, founder of Microsoft.
With huge amounts of data are generated across power systems some even argue that AI will be the brain of future smart grids. Like other sectors, Artificial intelligence will not only impact the energy sector with industry-specific applications but also with sector agnostic solutions, such as bots, which can be used by for example utilities to service customers.
Electricity generation
The use of data is not new in the electricity generation industry per se. Gas and wind turbines have been equipped with sensors for decades. Today, one of the most promising aspects of artificial intelligence lies in being able to look ahead of the curve. Thanks to AI and large amounts of data, it is becoming possible to go from preventive to predictive asset maintenance allowing the operator to detect anomalies at the generation asset in a timely manner before its failure, and thus avoid costly, and sometimes dangerous, unplanned maintenance.
Transmission and Distribution
In transmission and distribution, SCADA systems and PMUs generate vast amounts of data. By augmenting these systems with AI capabilities, it becomes possible to predict failures in the grid and multiple other areas of the transmission and distribution of energy, similar to the abovementioned use case.
Supervisory control and data acquisition (SCADA) is a system of software and hardware elements that allows industrial organizations to Control industrial processes locally or at remote locations.
A phasor measurement unit (PMU) is a device used to estimate the magnitude and phase angle of an electrical Phasor quantity like voltage or current in the electricity grid using a common time source for synchronization.
Energy Consumption
Excessive energy consumption is a global problem that is being faced by developed and emerging countries alike. To achieve a more sustainable consumption of energy, artificial intelligence is being used to monitor the energy consumption behavior of individuals and businesses. For example, Alphabet’s Nest is a smart thermostat for homes that reduce energy consumption by adapting to user behavior.
Generally, AI can make the use of energy more efficient. An example of such is Bidgely, a company working to solve the challenges that utilities face in meeting their demand-side energy by engaging the end users directly. Based on live metering data, AI can engage the end consumers providing them with information about their energy consumption. Providing both the utility company and the end user with valuable consumption insights has the potential to both change the way electricity is sold as well as disrupt markets for the sale of home appliances.
ENERGY EFFICIENT BUILDINGS WITH AI
- COORDICY is a strategic DK-US interdisciplinary research project for advancing ICT (Information and Communications Technology) driven research and innovation in the energy efficiency of public and commercial buildings. The project aims to contribute to about 75% reduction in energy consumption in new buildings by 2020 and a 50% in existing by 2050.
- NASA Sustainability Base is simultaneously a working office space, a showcase for NASA technology and an evolving exemplar for the future of buildings.
ENERGY STORAGE NETWORK
- Stem- with the mission to build and operate the largest digitally connected energy storage network for their users.
- SpaceTime- insight asset analytics help asset-intensive companies to optimize operations by determining criticality and risks associated with assets and to make risk-based and return-based decisions.
- Bidgely- provides a SaaS solution empowering utilities to engage with their customers by providing personalized and actionable insights for smarter energy consumption.
With the emergence of decarbonization, decentralization and latest technologies, the global energy market is undergoing a massive shift. Companies around the globe are exploring ways to employ AI and relevant technologies to improve the accessibility of energy resources. The pressure is to cut carbon emissions and deploying methods to employ renewable energy sources and manage the increasing, unpredictable energy requirements.
Chief executive, MySpace Tom Anderson says: “The use of AI, in our case predictive machine-learning algorithms, enables the consumer to have foresight over their energy profile for the first time.”
AI is being used to manage energy usage in a device most of us use i.e., mobile phones. Such as Apple deploying screen time which studies your apps usage to ensure the battery is saved. Also, rarely used apps, which would previously consume power in the background are shut down.
The energy industry produces massive amounts of data. To turn this data into insights that can improve productivity and cut costs, major energy players from oil and gas giants to renewable companies are turning to artificial intelligence.
According to a recent report from Greentech Media, the American energy storage market officially hit a milestone in the fourth quarter of 2017, deploying over 1000 cumulative megawatt-hours of storage between 2013 and 2017. As storage capacity increases and new technologies emerge, artificial intelligence is helping make usage more efficient.
By monitoring the energy consumption behavior of individuals and businesses, artificial intelligence companies can offer solutions to optimize usage.
Spain’s Nnergix uses machine learning technology to forecast atmospheric conditions and weather, including the amount of hourly photovoltaic energy produced at power plants.
Google released a tool GOOGLE SUNROOF to calculate the impact of solar energy on households across the United States. The project uses several factors to come up with the amount of money saved by solar energy, including weather data, utility electricity rates, 3D modeling, and shade calculations.
The cost of electricity is a great concern globally and demand for clean. Reliable energy needs can be fulfilled with the use of AI. Enabling green energy is an AI application with potentially long term impact.
Renewable energy sources are inconsistent, there is a huge unpredictability associated which can be eradicated with the use of AI in energy. With the integration of artificial intelligence in renewable energy sources, an increase in energy efficiency does not seem far off.
