Artificial intelligence (AI) is playing an increasingly larger role in managing and forecasting intermittency in renewable energy systems. As countries grow more reliant on energy generated from clean sources, dealing with renewable energy intermittency has become a pressing need. Unlike coal and other fossil fuels that can produce energy on demand, renewable sources are most effective when the time, weather, and atmospheric conditions are ideal.
For instance, solar farms are at their most optimum during the day at moderate temperatures, while wind farms work effectively when they are installed on flat terrains with high-speed winds. Energy production drops and can even reach zero when these conditions aren’t met, potentially making green energy projects an unreliable source of energy.
Fortunately, stationary energy storage facilities can alleviate most of the issues caused by renewable energy intermittency by storing energy generated during peak production times and releasing it into the grid when energy production slows or stops.
Renewable energy projects are also incorporating artificial intelligence into their intermittency management strategies to better address the challenges caused by green energy intermittency. Wind and solar are particularly vulnerable to intermittency, as variations in wind speed and sunlight can cause a significant drop in energy production.
Consequently, leveraging more predictive and intelligent mitigation measures such as AI could ensure the reliability of green energy and help the U.S. achieve its decarbonization goals. Machine learning algorithms can be used to forecast green energy intermittency more accurately than the persistence and ARIMA statistical methods that are currently in use, allowing renewable energy projects to predict periods of reduced green energy production with more precision.
Solar energy providers are using artificial intelligence models to analyze cloud movement and weather patterns to make short- to medium-term predictions. These models include long short-term memory (LSTM), convolutional neural networks (CNNs), and support vector machines (SVMs). A 2019 study found that artificial intelligence models can improve forecasting accuracy by 35% compared to conventional prediction models.
The wind segment is also benefiting from advanced AI models that are making it easier for providers to better predict complex temporal and spatial volatilities that affect wind. A 2020 study notes that using AI models can help providers reduce their forecast error by up to 30%. Machine learning inputs combined with high-quality weather data have also made it easier for grid operators to get ready for sudden drops in wind energy generation.
As renewables take on a greater share of the global energy mix, artificial intelligence will be essential for managing their inherent variability. By improving the accuracy of forecasts, optimizing energy storage use, and supporting responsive grid operations, AI can help stabilize power systems and make them more adaptable.
These capabilities will be critical to building a cleaner, more resilient, and future-ready energy infrastructure which will make it possible for zero-emission vehicles from enterprises like Mullen Automotive Inc. (NASDAQ: MULN) to have their batteries topped up using renewables.
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