Renewable energy adoption is growing across the world. As renewables make up a larger portion of the world’s energy production, predicting future scenarios becomes more pressing. Thankfully, machine learning applications can bring several improvements to renewable energy forecasting.
Machine learning applications are a subset of artificial intelligence, where algorithms learn to identify patterns from data with minimal human intervention. Many companies use it to find ways to improve or predict upcoming changes that would affect their business. This pattern-based prediction can help renewable energy, too.
Since renewables rely on nature, their efficiency and production can vary widely. Better predictive tools can help energy companies and users make the most of these installations. Here’s how.
Predicting Energy Consumption
One of the most crucial parts of renewable energy forecasting is predicting how much energy users will consume. Unlike other power sources, renewables don’t generate electricity around the clock. Power companies need to understand demand so they can allocate energy appropriately, so as not to waste any.
People can monitor usage trends to predict upcoming consumption changes, but this can be slow and inaccurate. Since computers are generally better at data-heavy tasks than people, machine learning can spot trends and make connections faster. In the renewable energy sector, this looks like an algorithm analysing usage patterns to determine which areas will need more energy at any given time.
With this information, energy companies can deliver power to where it’s needed most. The result is less waste, less disruption, and more consumer satisfaction. Some machine learning algorithms can achieve this even with partial information, making them far more reliable than traditional approaches.
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Predicting Weather Conditions
Another unique issue with renewable energy forecasting is the weather’s impact. Since renewables rely on things like the wind and sun, varying weather conditions produce different amounts of power. Machine learning applications can help predict these more accurately than traditional models.
Most weather forecasting models rely on data from large geographic regions that don’t always accurately represent local conditions. Machine learning algorithms can process far more granular data in a similar or even shorter time. This precision and speed produces more reliable, relevant forecasts.
The same machine learning programs can then predict how the weather will impact energy production. If renewables will produce higher-than-average levels, energy companies can scale down fossil fuels and vice versa. Some days, wind power alone can produce half of the U.K.’s energy, while others, it barely generates anything, so this flexibility is crucial.
Predicting Market Movements
Renewable power vendors need to meet market demands to make sustainable energy more widespread. Like in any business, appealing to customers requires an understanding of consumer trends. Many companies use machine learning applications in this area, and so can renewable energy businesses.
With enough high-quality data, consumer actions are surprisingly predictable. Machine learning algorithms can forecast long-term market movements, so renewable energy companies can understand their audience. Since it can take time to adjust production or marketing strategies, predicting these consumer trends early is crucial.
If more renewable power vendors adapt to shifting markets, they’ll become more attractive to consumers. As a result, sustainable energy will spread faster, helping the world move towards a greener future.
Predicting Potential Issues
Another crucial part of renewable energy forecasting is determining where potential problems may arise. While renewables provide lower lifetime operating expenses, upfront equipment costs are typically high. If companies can predict when conditions may threaten the grid, they can prevent it, leading to considerable savings.
One of the most useful machine learning applications in this area is predictive analytics. In this process, machine learning algorithms look at how equipment is running to predict when it will need repair. That way, workers can prevent costly breakdowns and don’t have to perform any unnecessary maintenance.
While human inspectors can attempt to do the same thing on their own, they’re not as effective. One study found that AI-assisted predictive maintenance is up to 25.3% more efficient and 24.6% more precise. These savings can help make renewable installations more cost-effective, helping them grow further.
Machine Learning Makes Renewable Energy More Viable
Machine learning can help forecast many relevant factors impacting renewable energy. As a result, renewables will become more reliable, affordable, and desirable. With these improvements, they could overtake fossil fuels faster.
Renewable energy is promising on its own, but machine learning expands its potential. By making renewables a more viable alternative to fossil fuels, machine learning is improving sustainability across multiple industries.