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How Utilities Are Using AI and Machine Learning to Mitigate Power Fluctuations from Renewable Energy Sources

Artificial intelligence in renewable energy systems will soon create a strong impact that will be felt across the globe. Using AI for mitigating power fluctuations from renewable energy sources will prove to be a game changer. It will transform energy production as we know it and take us one step closer to relying exclusively on renewable energy.

As of now, a great deal of research and development is going into machine learning for renewable energy to the point at where it is only a matter of time before it becomes a reality. And considering the rate of progress, that moment may not be too far off.

Mitigating Power Fluctuations from Renewable Energy Sources

Forecasting algorithms powered by AI are steadily becoming better and more reliable. Consequently, such systems will soon be integrated with the power grid to balance load from renewable energy sources. As a result, renewable energy will become more dependable and easier to manage, thereby clearing the way for greater adoption.

One of the biggest players in the race for renewable energy AI is Google. The technology giant announced in February 2019 that it has developed an AI system called DeepMind that can forecast electricity generation from wind farms. The system was deployed for predicting electricity output from a 700 MW wind farm in the US.

The program uses neural networks to intelligently process available weather data and previous weather patterns. Based on this input, the system is capable of reliably calculating electricity output from the wind farm for the next 36 hours. Such timely and accurate forecasts reduce costs associated with renewable electricity and make them a more feasible and dependable option for steady power output that is easy to predict. Google was able to substantially improve the performance of the wind farm and boost its efficiency using AI-powered DeepMind. The performance of the AI-powered wind farm was 20% better than conventional wind farms.

This will prove to be a major boon for renewable energy since its adoption was hindered due to its inherently sporadic nature. Solar energy and wind energy can suffer from big fluctuations in electricity output due to changing weather conditions. But DeepMind can now efficiently predict electric output using weather data due to which renewable energy is now far more predictable and thus viable.

To put things in perspective, the use of AI in the global energy market is poised to reach $7.78 billion by 2024. The bulk of investments will come from countries like the USA, the UK, China, India, and Singapore. 

Advantages for Energy Sector Operators

The energy sector forecasts energy blocks for days, weeks, months and even a year. With accurate forecasts, the energy sector can negotiate better prices.

Accurate prediction of electric energy is necessary because utilities will be entering contracts promising to deliver a given amount of electricity. Not being able to provide the promised amount of electric power will result in breach of contract, failure to deliver and bad customer experience. Equally important are the fines and hefty penalties that utilities must pay if they fail to provide the due amount of electric power.

But with artificial intelligence for renewable energy, utilities will become far better at fulfilling their promises and supplying the required amount of electric contracts. This will improve customer experience and empower them so that electric utilities can incorporate the consumer’s perspective and garner their support. They will also be able to avoid hefty fines and onerous penalties that ensue when unable to provide the required amount of electric power.

Improvements to AI for Renewable Energy

The success of DeepMind evinces the tremendous development and progress that AI for renewable energy is undergoing. This is no longer a futuristic idea or a lofty goal. Dependable AI algorithms that can accurately forecast renewable power are now very much within reach. Research and development is underway to further improve the capabilities of these intelligent systems.

Some of the most important companies that are attracting investment and driving innovation include:

  • Innowatts
  • Autogrid
  • BrainBox AI
  • CNIuard
  • SparkCognition
  • Uniphone
  • Enel
  • Belectric

In October 2019, National Grid Partners invested around $102 million of their $250 million venture fund into various technology companies in the energy sector.

These systems are using weather data and patterns to provide an accurate prognosis of electric output from renewable sources. Intelligent models can process satellite images, cloud formation, air flow, pressure distribution and other factors pertaining to weather in the region to make accurate forecasts. Besides satellite images, the system also makes use of anemometers for wind data and fisheye cameras for analyzing cloud formation. These inputs allow for better weather prediction and energy forecasts.

Thus, machine learning for renewable energy can help utilities to improve their operations and performance. artificial intelligence in renewable energy systems will help sustainable and environmentally friendly technology to become better. It will alleviate several doubts about renewable energy that have slowed down its adoption. By making renewable energy sources more predictable and consistent, AI and machine learning will result in a steadier power supply from green energy. The consequent wider adoption of renewable energy means that we will be able to successfully reduce our reliance on fossil fuels.

How AI in Renewable Energy Can Reduce Pollution and Carbon Emissions

Thus, machine learning and artificial intelligence in renewable energy have a key role to play in the mitigation of climate change, carbon emissions and environmental pollution. These advances in AI and ML are highly crucial particularly in today’s era where the global electricity demand is growing fast with each passing year.

It is projected that as much as 15% of energy could be saved (and thus emissions) through AI-monitored devices.      

Furthermore, autonomous vehicles can reduce emissions by 4% via more efficient driving. In Pittsburgh, AI has been deployed at intersections to monitor and regulate the flow of traffic due to which travel times have gone down by 25% and engine idling has been reduced by 40 percent. Thus, AI has an indispensable role to play in our fight against global warming and environmental pollution.

This high demand for electricity creates a major dilemma since fossil fuel powered plants are unsustainable and damaging to the environment. To satisfy the growing electricity demand, more of these polluting and unsustainable power plants will need to be built.

The World Health Organization estimates that global pollution kills well over 4 million people each year around the world. It is an undeniable fact that much of this lethal pollution emanates from fossil-fuel power plants that use coal, gas and electricity to produce electricity.

But that’s not all! Fossil fuel power plants are also responsible for high carbon emissions, which is threatening the very future of our planet. Rising temperatures and sea levels pose a serious threat to humanity. Thus, governments, international bodies and the private sector are rushing to curtail fossil fuel electricity generation before it is too late.

Advancements in artificial intelligence for renewable energy will certainly help to remediate these critical threats.

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