Artificial intelligence, machine learning can transform renewable energy industry; here’s how

Artificial intelligence (AI) and machine learning (ML) have the capability to transform the renewable energy space and can be leveraged by power companies to get better forecasts, manage their grids and schedule maintenance. Consumers can also enjoy uninterrupted green energy and get upfront information about scheduled maintenance works in the grid that could result in power outages.

Adoption of electric vehicles and electrification of heating systems in the next 10-15 years will add complexity to energy grids across the globe. Reliance on a central utility to produce and transmit electricity will reduce as other sources start producing energy through solar panels, store it in batteries and electric vehicles and feed it back to the grid. Uploading and downloading of electricity by millions of individual devices could put pressure on the electric grid.

AI in grid management

Decentralised energy sources can use AI and ML to predict energy consumption in households, comparing data from a specific part of the year and previous years. They can then send the excess electricity they produce to the grid, while power companies direct it to where it is needed. The data also helps utilities to stay informed about the energy required in the upcoming days and manage their grids to prevent outage.

“If you think of distributed energy resources as individual musicians, a utility is a conductor keeping the orchestra in sync as AI composes the symphony in real-time,” Emmanuel Lagarrigue, former chief innovation officer at Schneider Electric, had written in an article on World Economic Forum.

Enabling AI in grid management will mean shifting from infrastructure-heavy legacy models to a grid that is more resilient and flexible. These assets will also have to ensure protection of customer data and privacy and cybersecurity at all times.

Policymakers will have to shift focus to renewable energy generation and incentivise distributed energy generation in homes and private industry. Global governance of AI software is required to ensure interoperability, transparency and equal access of energy, said experts.

AI in forecasting

One of the major challenges for renewable energy is the unpredictability as it is dependent on resources like sunlight, airflow and water. AI helps in overcoming this challenge by forecasting the weather.

ML technologies can be used to analyse current weather and historical data to forecast conditions. Utilities use this forecast data to better manage the energy systems.

“They plan for the problem and take the help of fossil fuels to keep the power supply uninterrupted,” Hadi Ganjineh, head of IT, integrated technology and innovation at Super Energy Corporation, wrote in Forbes.

If the data predicts good weather, power companies store the produced renewable energy and manage the load if the forecast is bad.

Maintenance

To run power grids efficiently, power companies can use AI and ML to predict the specific parts of the system that needs maintenance and inform the consumers about maintenance in the grid. This way consumers are also aware of the scheduled power cuts.

 

Original post: https://www.cnbctv18.com/energy/artificial-intelligence-machine-learning-can-transform-renewable-energy-industry-heres-how-11858722.htm

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